Method and system for detection of biological rhythm disorders

ABSTRACT

System, assembly and method are provided to facilitate reconstruction of cardiac information representing a complex rhythm disorder associated with a patient&#39;s heart to indicate a source of the heart rhythm disorder. The complex rhythm disorder can be treated by application of energy to modify the source of the rhythm disorder.

RELATED APPLICATIONS

This application is a continuation of application Ser. No. 13/081,411,filed Apr. 6, 2011, now issued as U.S. Pat. No. 8,700,140, which claimspriority to Provisional Application No. 61/342,016, filed Apr. 8, 2010,which is incorporated herein by reference in its entirety.

GOVERNMENT RIGHTS

This invention was made with government support under Grants R01 HL83359and HL83359-S1 awarded by the National Institutes of Health. Thegovernment has certain rights in the invention.

BACKGROUND

1. Field

This invention relates generally to the field of medicine and morespecifically to a method, system and machine for diagnosing, finding thesource for and treating irregularities and other disorders of biologicalrhythms. In particular, the present invention can be applied tominimally invasive techniques or surgical techniques to detect, diagnoseand treat the disorder. One embodiment directs this invention todisorders of heart rhythm, another to electrical disorders of the brainand nervous system and others to electrical or contractile disorders ofthe smooth muscle of the gastrointestinal and genitourinary systems.

2. Brief Description of the Related Art

Heart rhythm disorders are very common in the United States, and aresignificant causes of morbidity, lost days from work, and death. Heartrhythm disorders exist in many forms, of which the most complex anddifficult to treat are atrial fibrillation (AF), ventricular tachycardia(VT) and ventricular fibrillation (VF). Other rhythms are more simple totreat, but may also be clinically significant including atrialtachycardia (AT), supraventricular tachycardia (SVT), atrial flutter(AFL), premature atrial complexes/beats (SVE) and premature ventricularcomplexes/beats (PVC). Under certain conditions, rapid activation of thenormal sinus node can cause the heart rhythm disorder of inappropriatesinus tachycardia or sinus node reentry.

Treatment of heart rhythm disorders, particularly the complex ones ofAF, VF and VT, can be very difficult. Pharmacologic therapy isparticularly suboptimal for AF (Singh, Singh et al. 2005) and VT or VF(Bardy, Lee et al. 2005) and, as a result, there is considerableinterest in non-pharmacologic therapy. Ablation is a promising andincreasingly used therapy to eliminate heart rhythm disorders bymaneuvering a sensor/probe to the heart through the blood vessels, ordirectly at surgery, then delivering energy to the cause(s) for theheart rhythm disorder to terminate it. Ablation was initially used for‘simple’ disorders such as SVT, AFL, PVC, PAC, but is increasingly usedfor AF (Cappato, Calkins et al. 2005), VT (Reddy, Reynolds et al. 2007)and, to a lesser extent, VF (Knecht, Sacher et al. 2009).

However, ablation is often difficult because tools to identify andlocate the cause of the heart rhythm disorder are poor, hinderingattempts to deliver energy to the correct region to terminate andeliminate the disorder. In persistent AF, a highly prevalent form of AF,ablation has a one procedure success rate of only 50-60% (Cheema,Vasamreddy et al. 2006; Calkins, Brugada et al. 2007) despite lengthy4-5 hour procedures and a 5-10% rate of serious complications (Ellis,Culler et al. 2009) including death (Cappato, Calkins et al. 2009). Evenfor ‘simple’ disorders such as atrial tachycardia, tools do not exist tomake the diagnosis and suggest a likely successful ablation location.

Even the most sophisticated known systems display data that thepractitioner has to interpret, without directly identifying and locatingthe cause of the disorder to enable the practitioner to detect, diagnoseand treat it. This includes currently used methods, described in U.S.Pat. No. 5,662,108, U.S. Pat. No. 5,662,108, U.S. Pat. No. 6,978,168,U.S. Pat. No. 7,289,843 and others by Beatty and coworkers, U.S. Pat.No. 7,263,397 by Hauck and Schultz, U.S. Pat. No. 7,043,292 by Tarjanand coworkers, U.S. Pat. No. 6,892,091 and other patents by Ben-Haim andcoworkers and U.S. Pat. No. 6,920,350 by Xue and coworkers. Thesemethods and instruments detect, analyze and display electricalpotentials, often in sophisticated 3-dimensional anatomicrepresentations, but still fail to identify and locate the cause ofheart rhythm disorders, particularly for complex disorders such as AF.This is also true for patents by Rudy and coworkers (U.S. Pat. Nos.6,975,900 and 7,016,719, among others) that use signals from the bodysurface to ‘project’ potentials on the heart.

Certain known methods for identifying and locating causes for heartrhythm disorders may work in simple rhythm disorders, but there are noknown methods that have been successful with respect to identifyingcauses for complex disorders such as AF, VF or polymorphic VT.Activation mapping (tracing activation back to the earliest site) isuseful only for simple tachycardias, works poorly for AFL (a continuousrhythm without a clear ‘start’), and not at all for AF with variableactivation paths. Entrainment mapping uses pacing to identify siteswhere the stimulating electrode is at the cause of a rhythm, yet pacingcannot be applied in AF and even some ‘simple’ rhythms such as atrialtachycardias due to automatic mechanisms. Stereotypical locations areknown for the cause(s) of atrioventricular node reentry, typical AFL andpatients with early (paroxysmal) AF, but not for the vast majority ofpatients with persistent AF (Calkins, Brugada et al. 2007), VF and othercomplex disorders. Thus, no methods yet exist to identify and locate thecause of complex heart rhythm disorders such as AF (Calkins, Brugada etal. 2007).

As an example of systems for ‘simple’ rhythms with consistent activationfrom beat to beat is given by U.S. Pat. No. 5,172,699 by Svenson andKing. This system is based upon finding diastolic intervals, which canbe defined in ‘simple rhythms’ but no complex rhythms such as atrialfibrillation (AF) or ventricular fibrillation (VF) (Calkins, Brugada etal. 2007; Waldo and Feld 2008). Moreover, this system does not identifyor locate a cause, since it is examines diastolic intervals (betweenactivations) rather than activation itself. In addition, it is focusedon ventricular tachycardia rather than AF or VF, since it analyzesperiods of time between QRS complexes on the ECG.

Another example is U.S. Pat. No. 6,236,883 by Ciaccio and Wit. Thisinvention uses a concentric array of electrodes to identify and localizereentrant circuits. Accordingly, this will not find non-reentrant causessuch as focal beats. Moreover, this method of using feature anddetection localization algorithms will not work for complex rhythms suchas AF and VF, where activation within the heart changes from beat tobeat. It identifies ‘slow conduction within an isthmus of the reentrycircuit’, that are features of ‘simple’ arrhythmias such as ventriculartachycardia, but are not defined for AF and VF.

In a subsequent U.S. Pat. No. 6,847,839, Ciaccio and coworkers describean invention to identify and localize a reentry circuit in normal(sinus) rhythm. Again, this will not find causes for an arrhythmia thatare not reentrant but focal, from where activation emanates radially.Second, this patent is based on the presence in sinus rhythm of an“isthmus” for reentry, which is accepted for ‘simple’ rhythms withconsistent activation between beats such as VT (see (Reddy, Reynolds etal. 2007)). However, this is not accepted for complex rhythms withvarying activation paths such as AF or VF.

U.S. Pat. No. 6,522,905 by Desai is an invention that uses the principleof finding the earliest site of activation, and determining this to bethe cause of an arrhythmia. This approach will not work for simplearrhythmias due to reentry, in which there is no “earliest” site inreentry because activation is a continuous ‘circle’. This approach willalso not work for complex arrhythmias in which activation varies frombeat to beat, such as AF or VF.

However, even in simple heart rhythm disorders, it is often difficult toapply known methods to identify causes. For instance, ablation successfor atrial tachycardias (a ‘simple’ disorder) may be as low as 70%. Whensurgeons perform heart rhythm disorder procedures (Cox 2004; AbreuFilho, 2005) it is ideal for them to be assisted by an expert in heartrhythm disorders (cardiac electrophysiologist). Thus, ablating the causeof a heart rhythm disorder can be challenging, and even experiencedpractitioners may require hours to ablate certain ‘simple’ rhythmdisorders (with consistent beat-to-beat activation patterns) such asatrial tachycardia or atypical (left atrial) AFL. The situation is moredifficult still for complex heart rhythm disorders such as AF and VFwhere activation sequences alter from beat-to-beat.

The prior art for diagnosing rhythm disturbances often measures times ofactivation at a sensor. However, such prior art has been applied tosignals that, at each recording site, are quite consistent from beat tobeat in shape and often timing. These prior art solutions are extremelydifficult to apply to complex rhythms such as AF or VF where signals foreach beat at any site (‘cycle’) may transition between one, several, andmultiple deflections over a short period of time. When a signal, forinstance in AF, comprises 5, 7, 11 or more deflections, it is difficultto identify which deflections is at the sensor (‘local’) versus a nearbysite (‘far-field’), as noted in studies to analyze AF rate (Ng andcoworkers, Heart Rhythm 2006). In another recent report, signals inrhythms, such as AF, require ‘interactive methods’ to identify localfrom far-field activations (Elvan et al. Circulation: Arrhythmias andElectrophysiology 2010).

In the absence of methods to identify and locate causes for human AF,physicians have often turned to the animal literature. In animal models,localized causes for complex and irregular AF (induced by artificialmeans) have been identified and located in the form of localized‘electrical rotors’ or repetitive focal beats (Skanes, Mandapati et al.1998; Warren, Guha et al. 2003). In animals, rotors are indicated bysignals that show a high spectral dominant frequency (DF) (a fast rate)and a narrow DF (indicating regularity) (Kalifa, Tanaka et al. 2006).Such uses of spectral dominant frequencies are described in U.S. Pat.No. 7,117,030 issued to Berenfeld and coworkers.

Unfortunately, these animal data have not translated into effectivehuman therapy. Animal models of AF and VF likely differ from humandisease. For instance, animal AF is rarely spontaneous, it rarelyinitiates from pulmonary vein triggers (that are common in humanparoxysmal AF). Both AF and VF are typically studied in young animalswithout the multiple co-existing pathology (Wijffels, Kirchhof et al.1995; Gaspo, Bosch et al. 1997; Allessie, Ausma et al. 2002) seen inolder humans who typically experience these conditions.

In AF patients, sites where rate is high (or, sites of high spectraldominant frequency, DF) have not been useful targets for ablation. Arecent study by Sanders and coworkers showed that AF rarely terminatedwith ablation at sites of high DF (Sanders, Berenfeld et al. 2005a).Other studies show that sites of high DF are common in the atrium, andablation at these sites does not acutely terminate AF (as would beexpected if high DF sites were causes) (Calkins, Brugada et al. 2007).In part, this may be because the DF method that is effective in animalsmay be inaccurate in human AF for many reasons, as shown by many workers(Ng, Kadish et al. 2006; Narayan, Krummen et al. 2006d; Ng, Kadish etal. 2007). Nademanee and coworkers have suggested that signals of lowamplitude with high-frequency components (complex fractionated atrialelectrograms, CFAE) may indicate AF causes (Nademanee, McKenzie et al.2004a). This diagnostic method has been incorporated into commercialsystems by Johnson and Johnson/Biosense. However, this method has alsobeen questioned. Oral and coworkers showed that ablation of CFAE doesnot terminate AF or prevent AF recurrence alone (Oral, Chugh et al.2007) or when added to existing ablation (Oral, Chugh et al. 2009).

Several inventions in the prior art acknowledge what was felt true untilnow—that AF is a “cardiac arrhythmia with no detectable anatomicaltargets, i.e., no fixed aberrant pathways,” such as U.S. Pat. No.5,718,241 by Ben-Haim and Zachman. This patent, as a result, does notidentify and locate the cause for a heart rhythm disorder. Instead, itfocuses treatment on heart geometry by delivering lines of ablation to“interrupt each possible geometric shape.” This patent creates maps ofvarious parameters of the heart.

Many inventions use surrogates for the actual cause for a cardiacarrhythmia, without identifying and locating said cause. For instance,U.S. Pat. No. 5,868,680 by Steiner and Lesh uses measures oforganization within the heart, that are constructed by comparing theactivation sequence for one activation event (beat) to the activationsequence for subsequent beats, to determine if “any spatiotemporal orderchange has occurred”. However, that invention assumes that organizationis greatest near a critical site for AF and is lower at other sites.However, this assumption may not be correct. In animal studies, indexesof organization fall with distance from an AF source, then actuallyincrease again as activation re-organizes at more distant sites (Kalifa,Tanaka et al. 2006). Moreover, U.S. Pat. No. 5,868,680 requires morethan one beat. As a result, methods such as U.S. Pat. No. 5,868,680identify many sites, most of which most are not causes of AF. This lackof identifying and locating a cause for AF may explain why methods basedon organization have not yet translated into improved treatment toacutely terminate AF. Similarly, U.S. Pat. No. 6,301,496 by Reisfeld isbased on the surrogate of mapping physiologic properties created from alocal activation time and vector function. This is used to mapconduction velocity, or another gradient function of a physiologicproperty, on a physical image of the heart. However, this patent doesnot identify or locate a cause of a heart rhythm disorder. For instance,multiple activation paths in AF mean that the conduction path and thusconduction velocity is not known between the points used fortriangulation. In addition, in the case of a rotor, activation sequencesrevolving around, or emanating symmetrically from, a core region mayactually produce a net velocity of zero.

For these reasons, experts have stated that “no direct evidence ofelectrical rotors has been obtained in the human atria” in AF (Vaquero,Calvo et al. 2008). Thus, while it would be desirable to identify (andthen locate) localized causes for human AF, this is not currentlypossible.

For human AF, particularly persistent AF, the absence of identified andlocated causes means that ablation is empiric and often involves damageto approximately 30-40% of the atrium that could theoretically beavoided if the cause(s) were identified and located for minimallyinvasive ablation and/or surgical therapy (Cox 2005).

Human VT or VF are significant causes of death that are poorly treatedby medications (Myerburg and Castellanos 2006). Treatment currentlyinvolves placing an implantable cardioverter defibrillator (ICD) inpatients at risk, yet there is increasing interest in using ablation toprevent repeated ICD shocks from VT/VF (Reddy, Reynolds et al. 2007).Identifying and locating causes for VT may be difficult and ablation isperformed at specialized centers. In VF, animal data suggest that causesof VF lie at fixed regions near His-Purkinje tissue (Tabereaux, Walcottet al. 2007), but again this is very poorly understood in humans. Theonly prior descriptions of identifying and locating causes for VFrequired surgical exposure (Nash, Mourad et al. 2006) or were performedin hearts removed from the body after heart transplant (Masse, Downar etal. 2007)). Thus, minimally invasive ablation for VF focuses onidentifying its triggers in rare cases (Knecht, Sacher et al. 2009) butcannot yet be performed in a wider population.

Existing sensing tools are also suboptimal for identifying and locatingcause(s) for complex disorders such as AF, including single ormulti-sensor designs exist (such as U.S. Pat. No. 5,848,972 by Triedmanet al.). However, such tools typically have a limited field of view thatis inadequate to identify causes for AF, that may lie anywhere in eitheratria and vary (Waldo and Feld 2008). Alternatively, they may require somany amplifiers for wide-area sampling that they are impractical forhuman use. Wide area sampling is advantageous and, in animals, isachieved by exposing the heart surgically (Ryu, Shroff et al. 2005) orremoving it from the body (Skanes, Mandapati et al. 1998; Warren, Guhaet al. 2003). In humans, even surgical studies only examine partialregions at any one time (for instance (Sahadevan, Ryu et al. 2004)), andintroduce problems by exposing the heart to air, anesthesia and otheragents that may alter the rhythm disorder from the form that occursclinically.

Thus, prior methods have largely focused on mapping of the anatomy toidentify whether a patient has a heart disorder, rather than determiningthe cause or source of the disorder. Thus, there is an urgent need formethods and tools to directly identify and locate causes for heartrhythm disorders in individual patients to enable curative therapy. Thisis particularly critical for AF and other complex rhythm disorders forwhich, ideally, a system would detect localized causes for ablation byminimally invasive, surgical or other methods.

SUMMARY

The present invention discloses systems, assemblies and methods tofacilitate reconstruction of cardiac information representing a complexrhythm disorder associated with patient's heart to indicate a source ofthe heart rhythm disorder. The complex rhythm disorder can be treated byapplication of energy to modify the source of the rhythm disorder.

This invention is a significant advance over the prior art. For example,unlike U.S. Pat. No. 5,718,241, our invention identifies and locatescauses (targets) for AF and other rhythm disorders that may stay atapproximately the same location within the heart for hours (see ourexample in a 47 year old man). Unlike U.S. Pat. No. 6,847,839, thepresent invention is capable of finding sources that transiently appearor may move (they are “functional”), that may explain the variations inAF. Unlike U.S. Pat. No. 5,868,680, our invention directly identifiesand locates cause(s) for a heart rhythm disorder, using as little as oneactivation event (beat) as shown in our examples. Unlike U.S. Pat. No.6,301,496, our invention directly identifies and locates electricalrotors, in which activation revolves around a core region, or focalbeats with activation radiating radially therefrom.

In one aspect of the invention there is provided a system to reconstructcardiac information representing a complex rhythm disorder associatedwith a patient's heart to indicate a source of the complex rhythmdisorder, the system including:

at least one computing device configured to:

receive cardiac information signals from the patient's heart during thecomplex rhythm disorder,

classify the cardiac information signals into high and low confidencesignals, wherein the high and low confidence signals are separated by aconfidence threshold,

determine activation onsets associated with the low confidence signalswithin an acceptance window;

order the activation onsets associated with the low confidence signalsand activation onsets associated with the high confidence signals; and

output the activation onsets associated with the high and low confidencesignals to indicate a source of the complex cardiac rhythm disorder.

In another aspect of the invention there is provided an assembly tofacilitate reconstruction of cardiac information representing a complexrhythm disorder associated with a patient's heart to indicate a sourceof the complex rhythm disorder, the assembly including:

a catheter comprising a plurality of sensors configured to providecardiac information signals; and

a computer-readable medium operatively couplable to the sensors, thecomputer-readable medium comprising instructions, which when executed bya computing device, cause the computing device to reconstruct cardiacinformation representing a complex rhythm disorder associated with apatient's heart to indicate a source of the complex rhythm disorder by:

receiving cardiac information signals from a plurality of sensors duringthe complex rhythm disorder;

classifying the cardiac information signals into high and low confidencesignals, wherein the high and low confidence signals are separated by aconfidence threshold;

determining activation onsets associated with the low confidence signalswithin an acceptance window;

ordering the activation onsets associated with the low confidencesignals and activation onsets associated with the high confidencesignals; and

outputting the activation onsets associated with the high and lowconfidence signals to indicate a source of the complex cardiac rhythmdisorder.

In yet another aspect of the invention there is included a method ofreconstructing cardiac information representing a complex rhythmdisorder associated with a patient's heart to indicate a source of thecomplex rhythm disorder, the method including:

receiving cardiac information signals from a plurality of sensors duringthe complex rhythm disorder;

classifying, by a computing device, the cardiac information signals intohigh and low confidence signals, wherein the high and low confidencesignals are separated by a confidence threshold;

determining, by the computing device, activation onsets associated withthe low confidence signals within an acceptance window;

ordering, by the computing device, the activation onsets associated withthe low confidence signals and activation onsets associated with thehigh confidence signals; and

outputting, by the computing device, the activation onsets associatedwith the high and low confidence signals to indicate a source of thecomplex cardiac rhythm disorder.

In another aspect of the invention there is provided a system toreconstruct cardiac signals associated with a complex rhythm disorderreceived over a plurality of channels from a patient's heart, the systemincluding:

at least one computing device configured to:

identify a plurality of discernable beats on high-confidence channelsthat are adjacent to a low-confidence channel, the discernable beats onthe high-confidence channels corresponding to a non-discernable beat onthe low-confidence channel;

compute a vector between at least two activation onsets of theidentified discernable beats on the adjacent channels through thenon-discernable beat on the low-confidence channel;

define a time interval associated with the non-discernable beat about aregion where the wave path crosses the non-discernable beat, the timeinterval indicating how early the non-discernable beat can activatebased on a previous beat on the low-confidence channel that has aselected or determined activation onset and how late the non-discernablebeat can terminate based on at least one predetermined property; and

select a possible activation onset during the defined time interval thatis closest to the computed wave path for the non-discernable beat

In another aspect of the invention there is provided an assembly toreconstruct cardiac signals associated with a complex rhythm disorderreceived over a plurality of channels from a patient's heart, theassembly including:

a catheter comprising a plurality of sensors to receive the cardiacsignals; and

a computer-readable medium operatively couplable to the sensors, thecomputer-readable medium comprising instructions, which when executed bya computing device, cause the computing device to:

identify a plurality of discernable beats on high-confidence channelsthat are adjacent to a low-confidence channel, the discernable beats onthe high-confidence channels corresponding to a non-discernable beat onthe low-confidence channel;

compute a vector between at least two activation onsets of theidentified discernable beats on the adjacent channels through thenon-discernable beat on the low-confidence channel;

define a time interval associated with the non-discernable beat about aregion where the defined vector crosses the non-discernable beat, thedefined time interval indicating how early the non-discernable beat canactivate based on a previous beat on the low-confidence channel that hasa selected or determined activation onset and how late thenon-discernable beat can terminate based on at least one predeterminedproperty; and

select a possible activation onset during the defined time interval thatis closest to the computed vector for the non-discernable beat.

In yet another aspect of the invention there is included a method amethod of reconstructing cardiac signals associated with a complexrhythm disorder received over a plurality of channels from a patient'sheart, the method including:

identifying a plurality of discernable beats on high-confidence channelsthat are adjacent to a low-confidence channel, the discernable beats onthe high-confidence channels corresponding to a non-discernable beat onthe low-confidence channel;

computing a vector between at least two activation onsets of theidentified discernable beats on the adjacent channels through thenon-discernable beat on the low-confidence channel;

defining a time interval associated with the non-discernable beat abouta region where the vector crosses the non-discernable beat, the timeinterval indicating how early the non-discernable beat can activatebased on a previous beat on the low-confidence channel that has aselected or determined activation onset and how late the non-discernablebeat can terminate based on at least one predetermined property; and

selecting a possible activation onset during the defined time intervalthat is closest to the computed vector for the non-discernable beat.

The systems, assemblies and methods are described hereinbelow in greaterdetail.

BRIEF DESCRIPTION OF THE DRAWINGS

The drawings constitute a part of this specification and includeexemplary embodiments to the invention, which may be embodied in variousforms. It is to be understood that in some instances various aspects ofthe invention may be shown exaggerated or enlarged to facilitate anunderstanding of the invention.

FIG. 1 is a depiction of the heart showing the use of sensors, ablationcatheter and the electronic processing components of the presentinvention which processes signals from the heart and orders them inaccordance with the invention.

FIG. 2 shows a sensor apparatus design of the present invention thatdetects biosignals for a wide area of the heart chamber at lowresolution, then for a narrower area at higher resolution.

FIG. 3 shows another sensor apparatus design of the present inventionthat detects biosignals for a wide area of the heart chamber at lowresolution, then for a narrower area at higher resolution.

FIG. 4 shows another sensor apparatus design of the present inventionthat detects biosignals for a wide area of the heart chamber at lowresolution, then for a narrower area at higher resolution.

FIG. 5 illustrates some signal types from the heart to be analyzed bythe invention, and defines some selected terms including activationonset, activation offset and diastolic interval.

FIG. 6 is a flowchart showing analysis of signals at multiple locationsto identify and locate causes for biological rhythm disorders inaccordance with the present invention.

FIG. 7 shows an embodiment of the invention depicting computation ofrate-behavior (restitution) curves for human signals, with insertion ofphysiological patterns in some cases.

FIG. 8 shows that rate-response (restitution) of human monophasic actionpotential duration may differ when measured between paced rhythms andAF.

FIG. 9 shows direct assignment of phase.

FIG. 10 is a flowchart of an embodiment, showing how sensed signals andstored data in a database can be used to create and use a probabilitymap to improve clarity for identifying and localizing causes for abiological rhythm disorder.

FIG. 11 is an example of use of the invention in a 47 year old man.Shown is a selection of signals (electrograms) from within the left andright atria and coronary sinus of a patient with atrial fibrillationpresenting for therapy.

FIG. 12 shows the results of using the method and system of theinvention, which identified an electrical rotor and located it to theright atrium. The activation trail is seen to revolve around a coreregion. The core region is also shown in the atrial geometry from thispatient as a dark dot in the lateral wall of the right atrium

FIG. 13 shows that, during direct ablation at the core region identifiedin FIG. 12 for less than 6 minutes, the AF slowed and terminated tonormal rhythm (sinus rhythm), thus demonstrating that the cause of theAF had in fact been located and successfully treated.

FIG. 14 shows that, after the AF had been terminated, it was notpossible to re-start the AF even by pacing the atria very rapidly (cyclelength 230 ms, equivalent to over 260 beats/min). Faster rate pacing wasnow blocked (did not stimulate the atrium).

FIG. 15 shows other patient examples of localized causes of human AFdetected with this invention. Electrical rotors are shown in twopatients in the left atrium. To the best of our knowledge, these are thefirst actual demonstrations of the existence of electrical rotors inhuman AF.

FIG. 16 shows another example of a localized focal beat cause of AF in a56 year old patient. The figure shows a focal beat cause in the leftatrium where the activation trail shows activation emanating radiallytherefrom. Ablation at this location also acutely terminated AF.

FIGS. 17A-17C show a method of reconstructing cardiac signals associatedwith a complex rhythm disorder received over a plurality of channelsfrom a patient's heart.

FIG. 18 shows a series of reconstructed action potentials and a failureof the reconstructed action potentials to conform to a detectedactivation onset.

FIG. 19A shows a plurality of time-varying signals obtained from sensorsreceiving cardiac (electrical) activity from a patient's heart during acomplex rhythm disorder (atrial fibrillation). The multiple deflectionspresent in many signals, and the varying signal characteristics even atthe same sensor location are noted, and make determination of eachsignal onset challenging.

FIG. 19B shows just that portion of electrical activity within a windowshown in FIG. 19A.

FIG. 19C shows an expanded view of a signal, for which a signaldetection is excluded because it falls within the rate-adjustedactivation potential duration (APD) and thus is taken as an artifact.

FIG. 19D is a two-dimensional representation of cardiac sensor positionsor electrodes, which provides a grid on the patient's atrium.

FIG. 20A shows examples of various methods for detecting beats,determining activation onsets, and disregarding noise in thetime-varying cardiac signals shown in FIGS. 19A and 19C.

FIG. 20B shows signals from a low-confidence channel.

FIG. 20C shows signals from complex and low-confidence channels, inwhich the shapes of individual beat signals vary widely from beat tobeat and thus the activation onset is very difficult to determine.

FIGS. 21A and 21B provide additional details to those shown in FIGS. 19Band 19D, respectively, to define a method of determining activationonsets for class B-beats using vectors.

FIGS. 22A-22C show displays of the reconstructed wave paths infibrillation from selected activation onsets according to the methodsand systems described herein.

FIG. 23A shows a two-dimensional representation of a matrix of sensors,which are shown as points or electrode positions superimposed on acardiac atrial surface.

FIG. 23B shows time-varying cardiac signals obtained from nine (9) ofthe cardiac electrodes or sensors shown in FIG. 23A.

FIG. 23C shows the result of tagging activation onsets for beats in eachof the raw signals shown in FIG. 23B in accordance with the systems andmethods described herein.

FIG. 23D shows a reconstruction of the activation potential duration(APD), which starts at the activation onsets determined in FIG. 23C andextends for a specified time or decay thereafter.

FIG. 24A shows an example display obtained from the raw signals shown inFIG. 23B using conventional methods known in the art.

FIG. 24B shows an example display derived from the tagging of activationonsets in FIG. 23C, in which a rotor is shown.

FIG. 24C shows a display in which the tagged activation times determinedin FIG. 23C and the reconstructed APD's determined in FIG. 23D are usedto define the intersection between a depolarization line and arepolarization line. This intersection is the core of the rotor, wheretherapy can be delivered to treat the rhythm disorder.

FIG. 25 is a block diagram of a computer system in accordance with thedisclosed embodiments.

DETAILED DESCRIPTION Definitions

For purposes of this invention the following definitions shall apply:

“Detecting/Diagnosing”: The terms detecting and diagnosing a rhythmdisorder are used interchangeably in this application.

“Activation time” means the time of activation onset for a given heartsignal.

“Activation time duration” means the time period and the signal waveformbetween the times of activation onset and offset for the signal of agiven heart beat. Diastolic interval is the time period from activationoffset of the prior beat to activation onset of the present beat (FIG.3).

“Activation trail” means the ordering of the activation time onset atthe sensor locations to create a discernible signature pattern, forexample, including without limitation a rotational pattern around a coreregion indicative of a rotor, a radially emanating pattern from a coreregion, indicative of a focal beat cause, or a dispersed pattern,requiring further signal sampling and repeating of above analysis steps.

“Identify and locate” means the process of discerning the presence of alocalized or dispersed cause of the heart rhythm disorder, then locatingsaid cause relative to sensor locations or relative to known anatomicpositions in the heart.

“Heart rhythm disorder” means an abnormal rhythm, often requiringtreatment. These include without limitation, rapid rhythms of the topchambers of the heart (atria) such as rapid and abnormal activation ofthe normal sinus node (inappropriate sinus tachycardia or sinus nodereentry), atrial tachycardia (AT), supraventricular tachycardia (SVT),atrial flutter (AFL), premature atrial complexes/beats (PAC) and thecomplex rhythms of atrial fibrillation (AF) and certain forms ofatypical atrial flutter. Rapid rhythms can also occur in the bottomchambers of the heart (ventricles), including such as ventriculartachycardia (VT), ventricular fibrillation (VF), torsades de pointes andpremature ventricular complexes/beats (PVC). Heart rhythm disorders canalso be slow, including sinus bradycardia, ectopic atrial bradycardiajunctional bradycardia, atrioventricular block and idioventricularrhythm.

“Cause of biological or heart rhythm disorder”, which is usedinterchangeably with “source of the biological or heart rhythm disorder”in this application, refers to, without limitation, a rotational patternof activation sequence around a core region indicative of a rotor, aradially emanating pattern from a core region indicative of a focal beatcause, or a dispersed pattern. In this invention, when a dispersed causeis found, signal sampling is extended to additional multiple locationsand the detection and analysis steps of the invention are repeated.These causes are directly responsible for the perpetuation of the heartrhythm disorder.

“Sensor”, which is used interchangeably with “electrode”, refers to anapparatus for detecting and transmitting signals from the heart or tothe heart.

Prior to the discovery of the present invention, the causes of humanbiological rhythm disorders, and particularly heart rhythm disorders,had not been identified. The present invention represents the firstknown instance where a method of detecting, diagnosing and subsequentlyeffectively treating, in an accurate and minimally invasive manner, thecause(s) that sustain, perpetuate, or ‘drive’ human biological disordershas been described. This method enables the physician to target thesesources for modification or elimination to abolish the disorder.Although one preferred embodiment is for minimally invasive proceduresfor heart rhythm disorders, the invention can also be applied tosurgical therapy, and for disorders of electrical impulse generation orpropagation in organs such as the brain, central nervous system (whereit may locate causes of epilepsy or seizure), peripheral nervous system(where it may detect tumors), skeletal muscle and smooth muscle such asthe gastrointestinal tract, bladder and uterus.

In accordance with an embodiment of the invention, there is disclosed anapparatus to sample signals, for example a sensor device such as aelectrode catheter from multiple locations within a human organ, such asthe human heart, at varying spatial resolutions and fields of view andwith apparatus to alter the number of sensing channels accordingly.

In accordance with an embodiment of the invention, there is disclosed amethod to identify and localize electrical rotors, focal beats and otherlocalized causes for heart rhythms, including complex rhythms such asAF, VF and polymorphic VT.

Embodiments of the invention may use processes and software methods suchas ordering the activation sequence to create an activation trail,processes such as the Hilbert transform, other phase delay methods,spatial coherence analysis and other methods.

In one embodiment of the invention, data collected from sensors andanalyzed is stored as data in a database that is automatically updated.This database is used to assist the physician in the diagnosis/detectionof localized causes, or to classify a pattern of causes of rhythmdisorders. This may take the form of a probability distribution map ofcauses in patients with specific characteristics.

In accordance with another embodiment of the invention, there isprovided an apparatus to display causes for the biological rhythm in aformat that can assist the physician in treatment. For example, a visualdisplay screen may be connected to a processor to allow for viewing ofthe activation trail and to allow for visual location of the core of arotor, focal source or other cause of the disorder. Audio formats mayalso be used alone or in combination with the visual format. Forexample, in addition to or instead of the visual depiction of the sourcesuch that the core can be visually identified, the coordinates of thesource and its core can be provided to the user by audio indications asto the location and cause of the disorder. Visual depiction isparticularly desirable because it provides the practitioner with a clearrepresentation of the cause and provides a reference for identifying thecore of the cause, which greatly facilitates the selection oftreatments. For example, a visual representation of the actual rotor orfocal beat allows the practitioner to accurately determine where todirect the ablation catheter or other treatment.

In accordance with another embodiment of the invention, once the causeof the disorder is identified, use of a treatment device or method, tomodify or destroy the site of an identified and localized source may beemployed to treat or eliminate the rhythm disorder. Non-limitingexamples of treatment devices and methods include the use of destructiveenergy (ablation) such as by ablation catheters, surgical ablationmethods, surgical removal or using devices inside the heart such asimplanted leads or other physical device, stimulating energy (pacing),direct delivery of pharmacologic agents, cellular therapy or otherintervention techniques. In one embodiment, a catheter capable ofsensing signals from the body, and particularly from the heart, may alsoinclude a means of treatment, such as the ability to delivery ablationenergy, stimulation energy, drug therapy, cellular therapy such as stemcells or gene therapy, or other treatment means. Thus, such a cathetermay be employed both in the detection and in the treatment of thedisorder.

The present invention is particularly suited for the detection,diagnosis and treatment of complex heart rhythm disorders such as, forexample, VF, polymorphic VT, torsade de pointes and AF, where once thelocalized cause is accurately identified and pinpointed, accurate andtargeted ablation of the localized cause may be implemented. Asdiscussed above, identification and physical location of the cause waspreviously not possible, and hence extraordinarily difficult even forexperienced practitioners to treat successfully, much less substantiallyameliorate or eliminate.

In addition to finding the cause of and subsequently treating complexheart rhythm disorders, the present invention may also be applied tohelp diagnose and treat ‘simple’ rhythms that emanate from a single siteby accelerating and simplifying analysis for the practitioner. For heartrhythm disorders, such simple disorders include focal atrialtachycardias, multifocal atrial tachycardias (MAT), sinus nodal reentryor inappropriate sinus tachycardia, ventricular tachycardia (VT),premature atrial complexes (PACs) and premature ventricular complexes(PVCs).

Included in the invention are a process and system to collect data,including sensing devices and recording systems The collected dataincludes at least the location of each sensor which transmitted one ormore signals and the onset time at which each activation signal oractivation time duration occurred. The processor receives thisinformation and sequentially orders the activation onset times. Theresult of this computation is the creation of an activation trail whichcreates a signature pattern for the disorder and indicates both thelocation and the type of the cause to the disorder, i.e. whether it is arotor, focal source or a dispersed pattern, i.e. no localized source,hence requiring further data to be collected from a different area ofthe heart or other body region. The data once ordered in this mannercreates an activation trail which can visually be depicted on a visualdisplay to show, in the case of a rotor source, the actual rotationalpattern of the rotor such that the core of the rotor is visuallyapparent and can easily be identified and hence treated. The same holdtrue for the depiction of a radially emanating source, such as a focalbeat. The sequential ordering of the activation onset times at eachsensor permits the location of focal rhythm disorders, such that thefocal core can be easily located on the visual display for targeted andaccurate treatment. Desirably, the rhythm sources or causes aredisplayed over a period of time to allow the practitioner to fullyobserve the causal point or area and to make a comfortable assessment asto the appropriate treatment at the causal location. In one embodimentthe data and/or the visual displays of the processed data (i.e. a“movie” of the activation trail) elucidates the signature pattern of thecause of the rhythm disorder. Such stored information allows for thepractitioner to consult previous patterns to aid in improving theidentification, localization and treatment of similar causes. In someinstances, such stored information allows for extrapolation of measuredreal-time data to provide predictive models or to clarify certainmeasured patterns using similar known patterns.

A further embodiment of the invention provides a process and system forthe treatment of such causes, often by modification or destruction oftissue where causes reside. Sixth, a preferred embodiment enables theinvention to be used in an ‘offline’, non-real-time review mode, ratherthan directly during a procedure to treat a patient.

The process and system of the invention may be employed to localizesources (i.e. find the physical location of the cause) for abnormalelectrical impulse generation or propagation in the brain or centralnervous system using the electroencephalogram or other index to guideinvasive therapy (surgery) or external beam irradiation to identify andtreat seizure or epileptic foci, or focal tumors (malignant orotherwise). The invention may also be used to identify sources forabnormal impulse propagation in striated muscle (such as injury inskeletal muscle), the gastrointestinal system (such as esophagealspasm), and the urogenital and respiratory systems. The invention mayalso be used to detect tumors (malignant or otherwise) in any bodysystem. The invention also has applications outside of medicine, such asfor locating the source of a seismic event or for locating energysources in tandem with methods such as radar or sonar.

The invention has several aspects to its process and system for carryingout the process. By way of example and not of limitation, in one aspectof the invention, signals are detected from multiple locations in anorgan in the rhythm disorder, altering the spacing between sensors tooptimize clarity of said sensing. A particularly desirable embodimentalso records these signals from a heart, or other body part, during arhythm disorder and stores them in a data base. The location of eachsensor associated with a particular signal, as well as the activationonset times at each sensor are transmitted to a processor for analysisincluding sequential ordering to form the activation trail identifyingthe cause of the disorder and its specific location in the body.Creating a database of causes, which may be manually or automaticallyupdated allows for accessing the data base to assist in theidentification and localization of disorder causes. This is used whendata collection in the current patient is of limited quality, to comparethe pattern in a patient to prior recorded rhythms in the patient todetermine if the rhythm is the same or different, or to compare thepattern in a patient to that from another patient, such as one withsimilar clinical characteristics. Previously stored data from a previouscase may be used to help identify, localize and display causes for therhythm disorder in a present case.

Visually displaying the sources of the disorder is extremely useful tothe practitioner because it serves as a visual guide to the existenceand location of the cause, and permits subsequent targeted and accuratetreatment to ameliorate or eliminate the rhythm disorder.

In other aspects of the invention, previously stored data from anothercase may be used to identify, localize and display causes for the rhythmdisorder in a present case. This can then be used to plan the use ofthis invention in a future procedure.

Description of Useful Components, Modules, and Devices

FIG. 1 shows a schematic of various useful components (modules) whichmay be used in the process and system of the invention. The modules maybe separate form each other and cooperatively interfaced to providetheir function, or one or more of them may be integrated with each otherof contained in the processor, such that the system has less separatehardware units. FIG. 1 depicts an embodiment which allows a cause of thedisorder to be localized during a minimally invasive percutaneousprocedure, or other procedures such as using surface ECG, amagnetocardiogram, an echocardiographic and/or Doppler measurements fromultrasound, electromagnetic radiation, sound waves, microwaves, orelectrical impedance changes.

In FIG. 1, electrical events in the heart 10 are recorded with sensingelectrodes. These electrodes may be catheters 20 placed within thechambers or vasculature of the heart, including custom-designedrecording catheters exemplified in FIGS. 2-4, The electrodes may also beextensions of leads from an implanted pacemaker orcardioverter-defibrillator, catheters used to record monophasic actionpotentials or other signals, that typically arrive via the vena cavae20-21 or coronary sinus 22. Thus, although particularly useful in theinvention, the process and system of the invention need not, however,employ the specialized catheters of FIGS. 2-4, as any catheters orsensing devices used inside or outside of the body which capable ofaccurately transmitting the activation times and location of theiroccurrence may be employed.

Electrodes 23 may record from the epicardial or pericardial surface ofthe heart, accessed via electrodes 21 in the coronary sinus, via theelectrodes 23 in the pericardial space or other routes. Electrodes maybe located in proximity to the nerves supplying the heart 15, which maybe located in the left atrium and ventricles. Electrodes may be virtual(computed) electrodes from a computerized mapping system, routine orhigh-resolution ECG mapping electrodes 30, electrodes implanted under oron the skin, or derived from methods to non-invasively detect signalswithout directly contacting the heart or body. Electrode information mayalso be derived from stored electrograms in a database 160.

An electrode 25 placed near the heart may be used to modify or destroyregions that are near or at the cause(s) for a rhythm disorder. If theelectrode is an ablation catheter, it interfaces to an energy generator60. Other electrodes may interface with a controller 40, and a pacingmodule 50, and all desirably communicate with a process controller 70.Ablation or pacing can be directed to nerves supplying the heart 15,which are located at many locations of the heart. Internal ablationelectrodes may be replaced with an external ablation system, such asexternal probes during surgery, or as in external focused irradiation orphoton beam as for cancer therapy. In addition, modification of sources,i.e. treatment of the causes of the disorder, may be achieved bydelivering appropriate pharmaceutical compositions, gene therapy, celltherapy, or by excluding tissue (at surgery or by using specializeddevices).

The process controller 70 may include various components or modules. Onsuch component or module includes a sampling module 80 which is capableof recording signals during the rhythm disorder, recording at variousrates not in the rhythm disorder (by pacing), and/or recording duringrates that simulate the heart rhythm disorder (by pacing or othermethods). Signal amplifiers (not shown) may be used to enhance thesignal clarity and strength, and the process controller may alsointelligently assign the fewest number of recording amplifiers to sensefrom a sufficient number of locations to identify and localize thecause. For instance, the system may use only 50-60 physical amplifierchannels to record from 128 sensors (for example, from two commerciallyavailable multipolar catheters), by recording those 128 sensors on a‘time-share’ basis by time-slicing, or by activating individual/multiplesensors close to a rhythm cause while deactivating others. This‘switching’ functionality may be performed by a switching component thatconnects the sensor device with the electronic control system, and thatmay be embodied in one or more other components. Switching may be manualor automatic, determined for instance on where causes of the heartrhythm disorder lie. Module 90 interfaces with the pacing module toprovide additional heart rates for sensing the biosignal. This isparticularly useful for the non-real time mode (mode 6), describedherein, because it can study the heart at different heart rates evenwhen not in the particular heart rhythm disorder being diagnosed andtreated.

The inventive method and system processes the collected data usinganalytical methods, which may be performed by analytic modules. Forexample, in FIG. 1, Module 100 is part I of an “Analytic Engine.” Thisportion of the Analytic engine determines the onset and offset for thebiologic signal over time, at each sensed location. This is implementedby creating a series of activation times (onset timing) and recoverytimes (offset timing) during the rhythm over time (illustrated in FIG.6). The signal is typically represented as voltage over time (that is,as a voltage-time series). Activation time can be processed in manyways. The simplest includes manual assignment at each location.Automated or calculated assignment can be achieved by using zero of thefirst derivative to define maxima or minima, zero of the secondderivative to indicate maximum upstroke or downstroke, or similarmethods. Activation onset and offset times can also be assigned when thevoltage time-series crosses a threshold. Another possible method toassign activation times is using pattern-matching. For instance, apattern selected to represent the activation duration can be correlatedto the signal at multiple timepoints over time. The time when saidcorrelation values are high indicate recurrences of said template, andthus are considered activation times. The template used for thisanalysis can also be obtained from stored data in a database, orcomputed from a rate estimate for the rhythm at that location.Simultaneous recordings from multiple sensors can help in analyzingactivation, particularly for complex rhythms such as AF or VF whensignal quality may be noisy, of poor quality or show multiple componentsat different times. From simultaneous recordings, a reference signal isselected, preferably at a nearby location to the channel being analyzed.Signals on the reference channel are used to select signal or signalcomponents on the channel being analyzed. This can be done by usingcomponents that retain a similar timing over time, using patternmatching or correlation functions, vectorial analysis or other methods.If many methods are required, heuristics, pattern recognition methodsand so-called ‘fuzzy logic’ approaches can be applied, constrained byknown pathophysiology of the atrium.

Module 110 is part II of the Analytic Engine that actually computes andlocalizes, i.e. determines the existence and location of sources(causes) for the heart rhythm disorder.

Some embodiments of the invention include a “Therapy Engine,” which maycontain one of more modules designed to cooperatively perform differentfunctions in the system and process. For example, module 120 in FIG. 1may be responsible for determining the location and migration pattern ofsources for the rhythm disorder within the heart. This may be a firstmodule of the Therapy Engine, and is used to compute the location andspatial region which is required to be modified in order to treat oreliminate the rhythm disorder. Treatment may be by delivery of ablationenergy or other means as discussed herein, and is not simply one pointor region if the source migrates during ablation. Module 130 isrepresentative of another module of the Therapy Engine, and desirablydirectly interfaces with the energy generator to ablate (destroy),modify (ablate or pace) or stimulate (pace) tissue at sites likely torepresent sources. Alternatively, the Module 130 may be used to modifytissue without destructive energy, for example by deliveringpharmaceutical agents, or gene or cellular therapies.

Module 170 of the system shown in FIG. 1 is representative of a tool todisplay the identification or location of causes visually or in auditoryfashion, to assist the physician in treating or eliminating the rhythmdisorder. For example, this module may include a display screen whichpermits the textual, graphic and/or auditory visualization on the screenof the rotor, focal or other cause of the disorder to be clearly seen bythe practitioner. In some embodiments, a “movie” clip of the disorderfound will be presented on the screen. This clip is a real-timepresentation of the actual cause and location of the disorder. Forexample, once the analysis of the data has been performed in accordancewith the process of the invention, i.e. the location of the signals andtheir activation onset times have been sequentially ordered, the resultof this analysis and computation will be shown on the screen in the formof an activation trail. If the pattern of the activation trail signifiesa series of activations revolving around a central core, then a rotorhas been found and is in fact a cause of the disorder. Similarly, if thepattern of the activation trail signifies a series of activations whichemanate radially from a central core region, then a focal beat has beenfound and is in fact a cause of the disorder. Thus, the inventiveprocess permits the direct finding of the cause of the disorder and theconvenient visualization of the existence, type and location of thedisorder for the practitioner. In the event that no discernable patternis found, i.e. the activation trail is not localized, then additionalsignal sampling by moving the sensor locations and/or turning-on alreadyplaced sensors may be appropriate. The additional signal samples maythen be processed in accordance with the invention and shown on thescreen. If a cause is found via the additional sampling and processingof the data, then a decision as to the appropriate treatment may bemade. In the event that a dispersed activation trail and pattern isfound, further additional sampling may be advisable until such time asthe practitioner feels is sufficient. In some instances, the result ofthe process will render a finding of the existence and location of arotor or a radially emanating focus. In other instances, where adispersed pattern remains even after repeated sampling and processing, adiagnosis may be made ruling out a rotor or focal beats as the cause.Thus, the finding of a rotor or a focal point (beat) will be essentiallya detection and diagnosis concurrently, whereas the lack of such afinding will be a diagnosis which may rule out the presence of either ofthese causes of the disorder.

Mode 1. Signal Sampling (FIG. 1, Reference 80)

Signal sampling can be done in real time, during a procedure to ablateor treat the rhythm disorder, beforehand to plan for a procedure, orafterwards to review the disorder. As stated above, signals arecollected at one or more locations from the organ using a variety ofsensor types. Contact sensors should maintain as good a contact with thetissue as possible. In the preferred mode, electrodes should record atmultiple sites simultaneously or nearly simultaneously. The fastestheart rhythm disorders such as AF have cycle lengths >100 ms, so thatsignal acquisition for substantially less than this time is considered‘nearly simultaneous’. An alternative mode of operation allows moving asensor to sequential sites. The invention may be used with any existingsensor apparatus.

Although a variety of commercially available electrode devices may beused to obtain signal sampling, particularly useful device embodimentsfor signal sampling are shown in FIGS. 2-4. These apparatuses usemultiple sensors that may be individually activated or deactivated, ormoved relative to one another. This enables adaptive spatial resolution,in that sensor spacing can be increased or decreased as desired.Widely-spaced sensors provide a wide field of view to ‘survey’ therhythm for a large portion of the organ (e.g. left atrium of the heart).Once the source location is approximated, the configuration is desirablyaltered to reduce sensor spacing for higher spatial resolution over anarrow field of view. A tightly spaced sensor configuration is preferredfor applying energy to a focused region to treat a source.

Adaptive spatial resolution is an important advantage of variousembodiments of the present invention. This can be achieved by physicallymoving sensors. FIG. 2 shows concentric helices (element 200), withmultiple sensing elements (electrodes or probes) for sensing signals andin some instances delivering energy or other treatment therapy (element205). The helices are widely spaced when parts of the catheter remainsnon-deployed (element 210) inside the shaft (element 215). Rotating andadvancing the assembly introduces more probes in the chamber, andreduces their spacing. FIG. 3 another embodiment of an inventive sensorcatheter in the form of an adjustable fan catheter, with multiplemeridians (element 230) each containing multiple sensing elements(electrodes or probes) (elements 240), also for sensing and in someinstances for delivering energy or other treatment therapy. By acombination of twisting or tortional motion along the shaft axis(element 245), as depicted in the Figures, the meridians may be morewidely spaced (element 230) or more closely spaced (element 235), i.e.spatially adjusted. FIG. 4 shows another embodiment of an inventivesensor catheter in the form of an adjustable corkscrew design, with asmall number of spiral meridians (element 260) ending on a bluntnon-traumatic end (element 270). As with the design structures of FIGS.2 and 3, the meridians of FIG. 4 may include multiple elements(electrodes or probes) (elements 265). The corkscrew can be advanced orretracted into the sheath by manipulating the shaft (element 280), toincrease or decrease the corkscrew size and/or probe spacing. Thesedesigns can be made larger or smaller to fit a larger or smaller organ(e.g. atria of varying sizes), or substructures including pulmonaryveins or the superior vena cava that may be sources for rhythms such asAF. Physical movement can be achieved manually by the physician orautomatically by using machines. Given the observed properties ofsources for heart rhythm disorders observed by the inventors, it isdesirable that the sensors sense from at least about 25% of the surfacearea of each one or more chambers of the heart. These designs areillustrative only, and are not intended to limit the actual physicaldesign or application of this invention.

Optimal contact for each sensor can be monitored by the processcontroller 70 for adequacy in various ways. For example, the processcontroller 70 can verify contact via stability in the amplitude ofsensed signals. Alternatively, the process controller 70 can conditionthe pacing module 50 to emit signals through electrodes 20-30, and usethe amplitude of evoked responses to verify contact. As a thirdalternative, the processing module 70 can determine contact byconfirming stable tissue impedance (in AF, for instance, where pacing isnot possible). As other alternatives, catheters designed to examine mildinjury patterns, or designed to directly measure contact force, can beused. In addition, catheter manipulation can be controlled roboticallyin semi-automated or automated fashion, as well as manually.

Adaptive spatial resolution can also be achieved electronically. Sensorsin this adjustable sensor device are connected to an electronic controlsystem that may activate or deactivate individual sensors. This may beperformed manually, such as if the physician wishes only to focus on oneregion of the organ, or automatically by the process controller in FIG.1 to focus on a region determined to be where the heart rhythm sourcelies. An electronic switching apparatus controls independent switchingof connections between the sensors and electronic control system, inorder to maximize use of a practical number of amplifier channels. Theseelectronic components may be embodied by various combinations oftraditional (wired) electrodes, fiber optics, etched-wafer circuitdesigns, biologic sensors, chemical sensors, pharmaceutical sensors,piezoelectric sensors, infrared sensors, patient-compliant opticalimaging, optrodes, remote sensors and other designs.

Electronic switching may also be achieved by time-slicing. A largenumber of locations may need to be sensed, but the number of sensingchannels may be limited. Signal time-slicing can record a larger numberof sensing channels from a smaller number of channels. For instance,signals are often sampled every 1 ms (at 1 kHz) although data acquiredevery 10 milliseconds (ms) or so is often sufficient for AF or VF sourceanalysis. Thus, the system can sense at location 1 for 3 ms, locations 2and 3 for 3 ms each, then return to sensor 1 to repeat the cycle at the10 ms timepoint. In this way, 90 locations can be sensed using 30channels. Any appropriate configuration can be used, depending on theswitching time in hardware or software, and allowing for noise factorswhen switching between channels. Many other methods can be used toincrease the effective number of channels, including sending multiplexedsignals along a fiber optic or other device, or storing signals inrandom access memory, then using off-line analysis to amplify andanalyze each in turn.

Numbers of sensed locations can also be increased using a combination ofsensors lying in contact with different heart planes. For instance,electrodes on the endocardial (inner) surface of the heart may becomplemented by electrodes on the epicardial (outer) surface andpossibly those in the heart muscle itself (via implanted electrodes) toincrease overall spatial resolution. This is of particular value in theatrium, whose wall is thin and where epicardial and endocardialelectrodes may target similar regions. In the ventricle, or in thickwalled regions of the atrium, different planes may provide differentinformation.

In certain preferred embodiments, sensing can be performed using one ormore sensors (probes) moved sequentially within the organ during theheart rhythm disorder. When a single probe is used, signals from eachlocation are aligned relative to a timing signal fiducial. This methodis easy to apply when a rhythm is relatively regular within the heart,such as the ‘simple’ disorders of focal atrial tachycardia or atrialflutter. However, this method can also be used as an approximate guideif the rhythm is irregular within the heart, such as the complex rhythmsof AF or VF. This has the advantage of requiring fewer sensors, and willwork if sources show some stability in space. For instance, while AF isirregular, activation may be regular at localized sources, for exampleat certain locations such as near the pulmonary veins.

One particularly useful embodiment for using sequential sensing atmultiple locations is now illustrated for a moving probe with twosensors (such as the two bipoles of a clinical quadripolar catheter),although more sensors may be applied if available. At each location, onesensor is considered the reference and the onset times for successivecycles (beats) are fiducials. The difference in activation time at thesecond sensor is used to indicate relative timing. The probe is nowmoved so that one sensor overlies the previously sensed location. Thesecond sensor now senses a fresh location and can record relative timingonsets for multiple beats here. The process is repeated for the entireregion of interest. Because this process introduces stability inrelative timing between locations, variability can be reintroducedstochastically using observed beat-to-beat timing variations at eachlocation.

An alternative approach is to use gradients in rate and/or organizationwithin the chamber, compared to stored data from a database for thatrhythm (including AF or VF). After sensing sequential locations, theactivation rate in both chambers is compared to stored patterns thatdescribe this relationship at various sources (rotors or focal beats)and surrounding sites. An error-minimization approach (such asleast-square-errors) may be used to estimate the source location.Estimates may be refined adaptively, based on similarity to subsets ofstored patterns and using algorithmic, heuristic, fuzzy logic or otherpattern recognition scheme. This process is repeated iteratively. For aspatially consistent source, second and subsequent iterations will addprecision to the original estimate, and may be focused at locationsclosest to the estimated source.

Delivery of treatment therapy may be another feature of the sensordevice, that will be described in detail later herein.

Mode 2. Computing Causes of Heart Rhythm Disorders

The first step in analysis is to determine the signal type, using alookup table as illustrated in FIG. 5, reference numerals 400-460. Thisstep determines if the signal arises from the heart (cardiac), brain,respiratory system, gastrointestinal tract, urogenital system, and soon. If cardiac, the signal may be a surface ECG, intracardiac,echocardiographic or other signal. If intracardiac, the signal isfurther classified as an action potential (monophasic action potential),bipolar electrogram, unipolar electrogram or other. Some of thesesignals provide high quality information (e.g. monophasic actionpotential recordings in the heart), while others do not. Lower qualitysignals are more likely to require pre-processing, filtering, averaging,comparison against stored signals in a database, in that patient atdifferent times and other computational steps to allow sourcelocalization.

In FIG. 6, the signal is parsed between steps 800-840 to identify itstype in the lookup table (from FIG. 5). This includes assigningactivation onset and offset, and the interval between beats (diastolicinterval) that depends upon the signal type illustrated in the lookuptable in FIG. 5. The lookup table can be a comprehensive biosignalinventory, with data on the distinct physiological role of eachcomponent for computational purposes. Components may vary with rate andmay fluctuate from beat to beat. Each signal component may reflect adistinct aspect of normal or abnormal physiology and thus indicatelikelihood that the rhythm disorder may arise. Examples are not intendedto limit the scope of the lookup table, which may include signals fromother muscles (e.g. skeletal muscle, bladder and gastrointestinaltract), the brain and the nervous system.

The next step in analysis is to define, for each sensed location, thephysiological signal to be analyzed. The goal is that the resultingsignal best represents actual physiological activation and recoveryoccurring in the heart rhythm disorder at each location. When therecorded signal is ‘clean’ (has a high signal-to-noise ratio), this willbe the physiological signal. If signals are noisy, then filtering, noisereduction and other schemes may be needed to reveal the physiologicalsignal. Said noise schemes may require recording while the patient holdshis/her breath for several seconds. For analysis of atrial rhythmdisorders, the physiological signal is best recorded between ventricularactivations (in the R-R interval), that may be facilitated if the heartbeat is reduced (R-R interval is prolonged) using agents to slowventricular rate or by reducing pacemaker rate in patients with suchdevices.

FIG. 7 panels 600-670 illustrate a particularly useful embodiment forconstructing physiological signals using computational methods tocompensate for limitations due to noisy or low quality data. First, theresponse to rate of each signal type (monophasic action potentials, MAP,illustrated in panels 600, 620, 640) is determined. This is performed bysensing signals at varying rates when in the rhythm disorder, or whennot in the rhythm disorder (such as by pacing, see mode 6). The responseof the signal duration (illustrated for MAP) to rate is shown in panels610, 630, 650, and shows that MAP shortens at increasing rate (that is,when diastolic interval shortens). It is to be noted that the responseto the same set of rates may vary when the patient is and is not in theheart rhythm disorder. FIG. 8, panels 700 to 740 show this. Pacing withdelivery of a single extrabeat in panel 700 results in the restitutionplot shown in FIG. 6, 710 as soon as AF begins. However, after severalminutes, the restitution curve changes as shown in panels 720-740.

One approach embodied in the present invention is to create a ‘hybrid’signal by inserting a physiological pattern at the time of eachactivation time onset (panels 660-670). The physiological pattern may beobtained by averaging recorded signals over time (algebraically, fromthe median beat average or other method), averaging signals atneighboring locations (spatial averaging), from monophasic actionpotentials at various locations (panels 660-670), by filtering existingunipolar or bipolar signals in the frequency or time-frequency domain,or by using stored patterns from a database (FIG. 1, 160). When storedsignals are used, properties including duration of these physiologicalpatterns may be adjusted for rate using rate-response (restitution)behavior. Stored signals may be obtained from this patient, anotherpatient with similar characteristics or another stored relationship.These processes may be applied to individual activations, or to theentire signal.

This method results in a physiological representation of activity ateach location over time that may otherwise be difficult to obtain in thebeating heart of patients during minimally invasive procedures. It hasapplications outside of heart rhythm disorders. For instance, saidphysiological pattern may be a model of cellular ion function. Thisenables the function of these ion currents at each sensor to be modeledcells timed to each observed activation, for the study of dynamics ofcalcium fluxes, potassium currents or other processes within the beatingheart of this patient. By way of a further example, this physiologicalpattern may be a model of a pharmacological ligand, allowing study onthe behavior of the beating heart to specific pharmacologic agents. Inthe gastrointestinal tract, cellular hormone release models may bestudied for each peristaltic ‘beat’. In the brain, known kinetics ofneurotransmitter or endorphin release for discrete brain waves(non-invasive, via the scalp electroencephalogram or invasive, assurgery) may help to understand and treat various conditions. Treatmentof conditions of epilepsy, for example, using the present invention isone embodiment of the invention. This invention also includes a methodfor determining the effect of a pharmacological or bioeffective agent onthe body by correlating the behavior of the beating heart or rhythm ofanother body part with the release, binding capacity or rate, or otheraction of the agent on the body.

An activation trail is then determined from sequences of activation inthe physiological signal at multiple locations. The simplest form ofthis analysis is to order activation at each location sequentially intime. In other embodiments, analysis may identify and locate causes fora rhythm disorder using frequency domain methods, time-domain methods orspatial-phase methods. Frequency domain methods include the Hilberttransform or wavelet transform or phase delay methods. Spatial phasemethods involve analyzing the spatial inter-relationships between sitesshowing activation at a certain location, in order to define theactivation trail.

Pertaining to phase-space methods, a well-known technique assigns aphase φ to the signal at every electrode and at every time point. Thephase at the exact location of the tip of the rotor is undefined andsumming up the phase of neighboring electrodes results in a “phase jump”of 2π. Thus, a rotor location corresponds to a phase singularity.Mathematically, these phase singularities can be found by evaluating aline integral over a closed curve as

{right arrow over (∇)}φ·d{right arrow over (l)}=±2 π where the lineintegral is taken over a path l surrounding the phase singularity. Sincethe signal from the electrode is a single observable, the determinationof the phase requires special attention. We will employ severaldifferent methods depending on the quality of the electrode signal.

The first phase-space method will be utilized if the signal from theelectrodes is noisy and/or has a small amplitude. In this case,activation times for each electrode will be determined, followed by anovel analysis of wave front dynamics. As a first step, the spatialresolution of the probes and their activation times may be increasedusing a bi-linear interpolation scheme that interpolates activationusing a fine regular grid created across the surface. In high qualityphysiological signals that contain activation, recovery and diastolicinterval information, this results in a time trace V(t) for each pointof the refined grid.

Since the shape of the action potential may be stable between beats, themethod next defines a mapping from the membrane potential V to the phaseφ. This map assigns a unique value of φ to each value of V such that themaximum and minimum of the phase variable differs by 2π. The detailedform of this map is arbitrary and the phase is computed usingφ=2π(V−0.5). The corresponding time trace of the phase variable resultsin construction of the signal and its phase instantaneously as in FIG. 8(panels 710-730).

Once the phase map is constructed the method will calculate, for eachtime, the sum of the phase for all four points of the fine regular gridseparated by a grid spacing that form a square (topological chargemethod). A result not equal to zero indicates the existence of a phasesingularity and a rotor. The analysis will be further aided by thetracking of wave fronts. The location of these fronts will be computedusing the regular fine grid by determining where and when V crosses athreshold value with a positive derivative dV/dt. Performing thiscalculation along the x and y direction of the fine regular grid andusing linear interpolation between the grid points, will result in a setof points that lie on the wave front.

The wave front is then constructed by connecting these points. A similaranalysis will be performed for phase, where isophase lines are tracked.A two-dimensional visual representation is then constructed that plotsfor each time point the value of the membrane potential using agrayscale or color scale, lines representing the wave fronts, linesrepresenting similar phase (isophase lines), and symbols locating thephase singularities. This visual aid will greatly benefit thepractitioner in interpreting the results of the inventive process andsystem. Note that the crossings of the lines representing the wavefronts and the isophase lines represent the phase singularity. Phasesingularities indicate core regions, and thus can be used to localizethe rotors.

The phase transform is able to demonstrate focal beats in AF—typicallyas centrifugal sources emanating from a localized area. A focal beat ischaracterized by a location that fulfills three criteria: 1) itsactivation time is earlier that at surrounding locations; 2) this regionwas previously inactive (in diastole) for a specified period of time; 3)the subsequent spread of activation emanates radially from the coreregion. Recognizing these 3 criteria, the invention finds these sourcesautomatically. This algorithm will first determine locations thatexhibit activation times ahead of their four nearest and fournext-nearest neighbors and mark these as potential focal sources. Next,it determines the activation times at locations surrounding a potentialfocal source. If the activation times of these locations are earlierthan their surrounding electrodes, the potential focal source isconfirmed and is marked accordingly. These sites are plotted using ourplotting technique as described above, greatly aiding the practitionerin localizing and interpreting these sources.

Alternatively, frequency domain methods may be used. On thephysiological signal during the heart rhythm disorder, that may be therecorded signal or a signal derived after filtering, noise reduction andother strategies described above, one may employ several methods.

Once such method is the Hilbert transform. The Hilbert transform shiftsthe phase of the negative frequencies of a signal by π/2 and the phaseof the positive frequencies by −π/2. In this approach, determination ofthe phase φ of the signal is achieved by plotting voltage against theHilbert transform of the voltage. The particularly useful embodimentapplies a detrending algorithm to set the voltages at the activationtimes (maximum dV/dt) to zero. The Hilbert transform is used toconstruct the phase plane of detrended signals. The Hilbert transform atall locations is interpolated across the fine regular grid createdacross the biological surface. Phase is then calculated from thestate-space plot of voltage versus its Hilbert transform. Again, thespatial distributions of phase will be analyzed with the topologicalcharge technique described above to locate phase singularitiesassociated with phase singularities (the ends of wavefronts) such as atthe tip of a reentrant wave. Activation wavefronts are constructed usingthe same technique as described above while isolines of zero phase willalso be tracked. An example of our methods in the human atria is shownin FIG. 12 elements 1030 and 1040 which show rotors in the left atriumcomputed using frequency-domain methods.

Another useful method employs a time delay embedding technique todetermine the phase of the signal. This technique consists of plottingV(t+τ)−V* vs. V(t)−V* for a fixed time delay τ and offset V*, resultingin a value of the phase φ for each time point and each location. Inpractice, the time delay and offset will be determined by thepractitioner after examining these plots for several locations usingdifferent values for τ and V*. Optimal values lead to trajectories thatdo not cross (that would lead to a non-unique value for the phase) andthat encircle the origin (ensuring that the minimum and maximum phasediffers by 2π). Both the signal and the phase are interpolated across afine regular grid created across the biological surface. The resultingphase map will then be examined for phase singularities and wave frontswill be tracked as described above.

Yet another useful method used to determine the phase of the signal is awavelet transform. The exact form of this wavelet is variable, and anexample includes the Haar wavelet. The wavelet transform will becomputed for each location. The wavelet allows us to view the signal inmultiple frequency resolutions. This will enable us to filter unwantednoise at specific frequencies (or frequency bands). In this approach,the phase transformation is achieved by plotting voltage against thephase shifted wavelet transform of the voltage. Once the phase φ hasbeen calculated, we will precede as before, including refining the gridthrough bi-linear interpolation, finding phase singularity and trackingwave fronts.

Other information, such as locations within the organ of sites of rapidrate during the rhythm disorder, the presence of very regular sitessurrounded by less regular sites, the presence of stable beat-to-beatconfiguration (shape) for successive signals as opposed to varyingsignal configurations, proximity to anatomic features known to beassociated with particular rhythm disorders (such as pulmonary veins inAF, His-Purkinje system in VF), or a combination thereof may also assistin identifying and locating sources.

Several types of activation trails may result, producing correspondingdiscernible signature patterns for various types of causes for a rhythmdisorder. An activation trail in which sequences of activation revolvearound a central ‘core’ region is termed a rotor. An activation trailthat emanates radially from a core region is termed a focal beat (or asite of repetitive focal activations or beats). Another activation trailtype is a dispersed pattern, in which a localized source is not clearlyidentified. In particularly useful embodiment, in such cases, signalsensing is repeated at additional locations or for additional periods oftime. Localization of a cause for a heart rhythm disorder is based onthe location of the core region and additional activation from thisregion. Some embodiments identify the core region directly. Forinstance, the Hilbert Transform and direct phase assignment methodsidentify the core region as the site where real and imaginary parts ofthe analysis intersect. In contrast, the direct sequential orderingmethod of the present invention indicates a core region either visuallyor analytically.

FIG. 10, referenced by panels 1400-1495 describe the process ofoptimally identifying, locating and selecting cause(s) that are mostlikely to indicate primary causes of the rhythm disorder. In oneparticularly desirable embodiment, a probability map 1480 for sources ofthe disorder is constructed. This indicates a likelihood that eachsensed location harbors a cause of the rhythm disorder, relative toother sensed locations. A higher relative likelihood is assigned forsites where core regions sustain for longer periods of time (or, formore rotations or beats), where the rate of activation is faster, wherethe rate of activation is more organized, that activate surroundingtissue in a 1:1 fashion (thus, there is electrogram linking) andactivate larger regions of tissue in phase (and thus have a large spaceconstant), when fewer concurrent sources are identified, for sourcesthat lie near known regions of high likelihood for rhythm disorders suchas the pulmonary veins in human AF, for sources with less migration overtime, and for rotor versus focal beat types of source. In oneparticularly useful embodiment, probabilities are assigned aftercomparison with stored examples in a database; the comparison may takethe form of a stepwise multivariate comparison. In the limit case, aspatially fixed source, that is a solitary electrical rotor and thatdirectly activates the entire organ is by definition a primary cause ofthat heart rhythm disorder.

Surrogates for the activation trail also exist. These are data thatapproximate the identification and localization provided by theinvention using data from fewer locations, less lengthy or detailedrecordings, or using information from other resources such as the ECGrather than from within the heart. Thus, surrogates enable approximationof the activation trail using a reduced number of sensor locationscompared to an analysis that directly measures the activation trail.These surrogates, used independently or in combinations, include sitesof rapid rate during the rhythm disorder, the presence of very regularsites surrounded by less regular sites, the presence of stablebeat-to-beat configuration (shape) for successive signals as opposed tovarying signal configurations, signals where amplitude is particularlylow, signals that are very prolonged for each activation is veryprolonged, proximity to anatomic features known to be associated withparticular rhythm disorders (such as pulmonary veins in AF, His-Purkinjesystem in VF), or a combination thereof may also assist in identifyingand locating sources.

Surrogates may be detected from the ECG, and thus be used to plan aprocedure or guide therapy in a patient. Vectorial analyses of the ECGfor regions of regularity and high rate, particularly if surrounded byregions of lower regularity and rate, indicate locations within theheart where sources lie.

FIG. 10, panels 1400-1495, summarize the approach to identify and locatesources. Panels 1400-1450 determine if sufficient sensor resolution ispresent to identify a cause. Criteria for sufficiency include theabsence of discontinuities in the wave front calculation, and absence ofjumps in the location of core regions, and an absolute sensor spacingthat should not exceed approximately 1 cm. This is based uponcomputations that the minimum circumference of a reentry wave is >2 cmin the human atrium and larger in the human ventricle. Panels 1460-1490then use a combination of optimized sensed data and stored data tocompute sources, that are then treated, panel 1495. The presentinvention includes the wide use of filtered or unfiltered clinical data,data from a database including this and other patients, or computationalestimates to represent the signal to be analyzed as well as the resultsof analysis. In addition, the hybrid use of existing patient-acquireddata, signal processing methods, numerical methods and stored signalsfrom a database are major advantages of the inventive process andsystem, particularly because high-resolution physiological data fromhuman atria or ventricles may be extremely difficult, if not impossible,to obtain at clinical electrophysiologic study without open heartsurgery.

All of the above approaches may be applied to any complex rhythm,including VF. Of course, these approaches may also be applied to “simplerhythms” such as reentry around an anatomical obstacle or rotorsanchored at scar tissue (such as atrial flutter).

These inventive processes may be implemented in software, operated veryquickly and are suitable for real-time, as well as off-line analysis,using small scale components such as those found in implantable devices,portable ambulatory machines, wristwatch-sized devices, as well aslarger scale computers found in electrophysiology laboratories.

Mode 3. Storing Data on Heart Rhythm Sources in Database

Data on sources for rhythm disorders desirably may be stored in adatabase 160. This may be useful to classify sources in differentpatients, to help identify sources in a single patient, or to determineif a patient has returned with the same or a different source. Data inthe database will thus include the characteristics described above,including the number of concurrent sources, rate, variability in rateover time, duration, size of biological organ whose activation isdirectly caused by the source (the space constant), location, whetherthis location migrates over time, rate within multiple regions of theheart at the time that the source was detected (such as left and rightatrial rate during AF), and the response of each source to ablation.

Additional information to be stored in the database include one or moreclinical factors from a group comprising gender (male/female), age,weight, height, presence of diabetes mellitus, blood pressure, atrialsize, ventricular size, regions of atrial or ventricular scar, the leftventricular ejection fraction.

In a particularly useful embodiment, the database of AF Sources 160 willbe continuously updated, based upon new source localization fromadditional cases. This will be used to help source localization forpractitioners studying new patients, by way of a software expert systemthat will match the new patient to already stored patterns.

Source data to be stored will be analyzed for consistency with existingdata, matched by the above variables. Only raw data that meets rigorousstandards for data integrity will be incorporated, others will berejected. After ensuring data integrity, data will be added to thedatabase to improve localization for future patients.

The invention and database interface may include an expert system thatcompares current data with stored data. Based on the closest match ormatches, logic within the invention determines if additional heartrhythm sources or additional characteristic should be studied, andwhether they may lie based on stored information. This uses a ‘goodnessof fit’ against various stored parameters. This functionality isincluded because in practice, the number of sensed locations is limitedby time constraints, in practice, many sensor locations may providesuboptimal data, thus limiting the actual sensed resolution, and becausethe inventor has observed that many patients show similar sourcelocations and characteristics.

Database updates will be available to the practitioner regularly from acentrally located, secured database that contains the above information.No information on patient name, geographical location, study date orother items prohibited by the Health Information Portability Act (HIPAA)will be included. This database will be maintained at a remote locationbut available electronically by means including wired and wirelesscommunication, electronic media such as CDs, DVDs, and solid statestorage devices.

Mode 4. Display of Sources of Biological Rhythm Disorder

The invention includes methods and apparatus to communicate theidentification, location and above characteristics of sources forbiological rhythm disorders to the practitioner. This includes a visualdisplay means, typically in the form of a graphical display on acomputer monitor, or a printout showing the source in relation tocardiac anatomy, or a basic textual line summary of the location and/orsensor site where the source lies.

An auditory display may also be used, that vocalizes the identification,location and above characteristics of sources for biological rhythmdisorders to the practitioner. In one embodiment, this would include theconclusions or a summary of analysis rather than the analysis resultsthemselves.

Mode 5. Therapy at Causes of Biological Rhythm Disorder

In addition to the processes and systems of the invention used to detectand diagnose the cause of the rhythm disorder, the invention alsoincludes devices and methods to treat the source for the biologicalrhythm disorder, in order to modify, ameliorate or eliminate said rhythmdisorder.

Treatment of the source may employ any useful technique, includingablation with radiofrequency, freezing energy, microwaves or othersources. Modification may also include cell therapy (such as with stemcells), gene therapy, pharmaceutical delivery, ionizing or non-ionizingradiation delivered by devices inside or outside the heart, or otherinterventions.

Treatment is delivered to modify the cause. In a simple heart rhythmdisorder such as atrial tachycardia or atrial flutter, energy is applieddirectly to eliminate the cause. In a complex rhythm disorder, such asAF, energy can be applied to ablate (destroy) the source, to isolate thesource by destroying tissue between the source and the remainder of theviable heart chamber, or to modulate the interaction between differentsources. This latter form of treatment is very novel and has been shownin experiments by the inventor to be extremely effective. Modulation maybe performed in a stochastic fashion.

In a particularly desirable embodiment, therapy is targeted at the coreregion of an identified or localized cause for the rhythm disorder, withthe intention of eliminating this cause to treat the heart rhythmdisorder. This may be applied sequentially to identify, locate and treatmore than one cause for said disorder.

Alternatively, therapy may be targeted at locations neighboring the coreregion for a source, with the intention of disconnecting the source fromsurrounding tissue.

Alternatively, therapy may be targeted at locations neighboring the coreregion for a source, with the intention of causing the source to migratetowards tissue where definitive treatment is more easily accomplished.For instance, if the source lies at a location where ablation isdifficult due to anatomy, tissue thickness or other factors, ablation onone side of the source may cause it to migrate towards a location thatis easier to ablate due to thinner tissue or anatomic factors.

Alternatively, therapy may be targeted at locations neighboring the coreregion for a source, with the intention of preventing movement of thesource and thus compartmentalizing it.

Alternatively, therapy may be targeted at locations neighboring the coreregion for a source, with the intention of reducing the mass of tissueavailable for the source to sustain and thus causing it to terminate.

Treatment may take the form of ablation, delivered via a catheter in theheart (element 25 in FIG. 1), on the epicardial surface, or an electrodepresent on one of the multi-electrode catheter designs included herein,for example see FIGS. 2-4.

When a dispersed activation trail is observed, locations where sourcesmay lie that are difficult to identify are targeted first. In patientswith AF, such sites include the pulmonary veins and other thoracicveins, and the atrial appendages. Thus, pulmonary vein isolation isperformed first, followed by therapy at additional sites if clinicallysuspected. Signal sensing is then repeated to identify and locate acause.

In preferred particularly desirable embodiment, the multi sensorcatheter (FIGS. 2-4) includes an assembly that can deliver therapy inthe form of ablation. In this embodiment, sensors at locations where thesource lies are activated to deliver ablation energy to modify oreliminate the source.

The system may deliver therapy in a spatial locus, as well as at fixedlocations. In this system, the location of the source core region isanalyzed constantly throughout therapy. Therapy, such as ablationenergy, is directed at varying locations and potentially multiplelocations to constrain movement of the source. An analogy is toconstruct a ‘fence’ of ablated tissue around a moving source in order tokeep it in one location. This may require therapy delivery (such asablation) at multiple sensors of said poles of said assemblyconcurrently. This process is continued until the rhythm terminates or aremote source becomes dominant.

This invention is well suited to target therapy performed surgically inthe operating room with direct exposure of the heart. This may be via aminimally invasive approach or traditional open chest heart exposure.The choice of recording electrode, sock, plaque or other equipment is upto the discretion of the surgeon and does not alter the principles oftherapy.

Alternatively, said modulation can be applied by stimulating (pacing)the tissue. For pacing, the process controller 70 conditions the pacingmodule 50, to stimulate the heart using electrodes in the heart 20-25,electrodes on the body surface 30, or electrodes elsewhere such as fromthe esophagus 150. The electrode controller 40 receives signals from theelectrodes before, during and after pacing. Pacing is used to increaseheart rate and introduce extra beats.

In alternative embodiment, the invention can ablate or stimulate cardiacnerves to modify or eliminate the source. Thus, if sources lie atlocations of heart ganglionic plexuses, ablation or pacing of suchlocations can be used to modify the source.

If the abnormal rhythm terminates after modify or eliminating sources,attempts can be made to restart the rhythm. In the case of heart rhythmdisorders, this may include very rapid pacing, the administration ofisoproterenol or other interventions. The entire application of thisinvention is then repeated.

In the event that the abnormal rhythm can no longer be initiated, thephysician may exercise the discretion to modify additional regions thatmay be potential sources. This information may be available directlyfrom stored data in the database, matching patients with a similarclassification to the current patient.

Mode 6. Non-Real-Time Review Mode

In an important mode of operation, the invention can be used in anon-real time, offline analysis fashion. This review mode can be appliedto data from this individual at another time, such as a priorelectrophysiologic study, data from a different device (such as animplanted pacemaker or defibrillator) or even a prior failed ablation.This can be used to review results from a prior procedure, to reviewdata from a patient prior to planning the application of this invention,or to assess if the same patient now presents with the same or adifferent source for their rhythm disorder.

Signals are first uploaded from stored electrograms in a database 160 tothe processor controller 70. This database can be the master databasethat stores data on multiple patients, or a patient-specific database.Data storage and retrieval can be implemented for any signal type.Stored signals can be derived from another source, a catalogued source,or computed or virtual signals such as from Ensite 3000 or NavX by StJude Medical, or Carto by Biosense-Webster. Signals may also be derivedfrom a different individual, querying the database for a patient withsimilar demographics and heart rhythm disorder.

In a separate non-real-time mode, data obtained when the patient is notin the heart rhythm disorder can be used by the invention to identifyand locate sources for a rhythm disorder. This may be useful, forexample, if the heart rhythm disorder is not observed at the time of aprocedure, and cannot be started using conventional methods. This modeuses biological properties of the chamber to predict locations wheresources/causes may lie when in the heart rhythm disorder. Such locationsinclude sites where the maximum slope of action potential durationrestitution is >1, sites where beat-to-beat oscillations in therepolarization signal shape or duration are observed, or whereconduction velocity restitution is broad to indicate slowed conductionat critical rates.

In the preferred embodiment, to measure restitution it is necessary tosense signals for a wide range of rates at each location, as indicatedin FIG. 1 element 90. This may be achieved using pacing. In this case,the process controller (FIG. 1, element 70) conditions the Pacing module50, to stimulate the heart using electrodes in the heart 20-25, on thebody surface 30, in the esophagus 150 or elsewhere. The wider the rangeof rates, particularly fast rates, the more comprehensive the data rangefor that signal for analysis of restitution. When pacing is not anoption, the invention will prompt the user to increase heart rate usingother options or to use stored information from a database.

In this embodiment, the rate-response (“restitution”) curve is createdat each rate for each component of signals shown in FIG. 5. For example,this step may compute how monophasic action potential duration (timefrom phase 0 to phase 3) varies with rate (APD rate restitution).Examples of atrial APD restitution are shown in FIGS. 5, 6 (items600-720). Using pacing to increase the range of sampled heart ratesprovides a comprehensive assessment of rate response of each biosignal.

FIG. 7, references 600, 620, 640 show a useful embodiment, wherebyrecordings of human action potentials made by the inventor in the leftatrium 420, each of which provides high quality information includingdepolarization (phase 0), repolarization (phases 1-3), phase 2 amplitudeand action potential duration (time interval from phase 0 to phase 3).Phase 4 indicates the interval between one beat and the next. Theinvention may determine rate response (restitution) of multiplecomponents, focusing on rate-response of AP duration (time from phase0-3), and AP phase II amplitude.

Reference 400 (FIG. 5) is an ECG. This includes intra-atrial components(the P wave and PR interval), and ventricular components includingdepolarization (the QRS complex) and repolarization (the T wave). Foratrium, the invention records how P-wave duration varies with rate,using analyses shown later in FIG. 7, 600-650. For the ventricle, theinvention records how QT interval varies with rate as a measure ofventricular APD rate-behavior (restitution). Individual QRS complexesare aligned using one of several columnar techniques, including methodsthat align electrograms about the point of largest positive or negativeslope, their peak values or minimize their mean square differences, ormetrics based on derived signals. T-waves are identified and alignedsimilarly. Atrial activity is considered to lie in the interveningintervals.

If the signal is a unipolar electrogram, it is also analyzed inanalogous fashion. Each is analyzed for waveform shape as well asduration. FIG. 5, Items 430-440 indicate unipolar electrograms from thehuman left atrium 430 and left ventricle 440 respectively, withdepolarization and repolarization measured collectively as theactivation-recovery interval, a surrogate for the monophasic actionpotential duration. The invention determines adjustment of variouscomponents for rate.

Signals can also be bipolar electrograms (items 450, 460), and theinvention determines rate response of each component.

In an alternative embodiment, ECG and electrogram data are uploaded froma database 160 for analysis in an analogous fashion to the describedreal-time mode of operation. Data from the database can be from the sameor different patients, recorded at any time and using any acquisitionsystem.

In AF, MAP restitution may differ from MAP when not in AF. FIG. 8element 700 shows the initiation of AF after pacing. Element 710 showsMAP restitution during pacing in black. Immediately after AF onset (redpoints), APDs track previously derived MAP restitution. However, thismay not be true for longer-lasting AF. Elements 720, 730 and 740 showpatients with long-lasting AF, in whom APD restitution differs from thatobtained in pacing prior to AF.

Thus, it may be advantageous to use APD restitution obtained from thepatient in AF, at this time or a previous time, or from stored APDs inthis or other patients, or filtered or computed data, for signalprocessing and analysis.

Locations where sources may arise during a subsequent heart rhythmdisorder may now be predicted from these analyses. For monophasic actionpotentials, site where the maximum slope of MAPD rate-behavior(restitution)>1 may be immediately adjacent to causes for VF or AF.Other indexes of high likelihood for the initiation of heart rhythmdisorders include broad rate-response (restitution) of conduction, sincesuch sites of dynamic conduction slowing may indicate sites where heartrhythm causes lie.

The energy generator 70 may be activated to apply destructive energy(either radiofrequency, cryoablation or microwave radiation) via theablation electrode 25. This electrode can be moved within the heartmanually by an operator, that is the traditional approach, or remotelyusing robotic or computer assisted guidance.

The implementation of the system described herein may be based largelyupon digital signal processing techniques. However, it should beappreciated that a person of ordinary skill in this technology area caneasily adapt the digital techniques for analog signal processing.

Various features of the invention are set forth in the following claims.

While the invention has been described in connection with particularlydesirable embodiments, it is not intended to limit the scope of theinvention to the particular form set forth, but on the contrary, it isintended to cover such alternatives, modifications, and equivalents asmay be included within the spirit and scope of the invention as definedby the appended claims.

EXAMPLES Identification and Localization of Source for AF in 47 Year OldMan

FIG. 11 panels 900-910 illustrate a representative patient, a 47 yearold man with persistent atrial fibrillation (AF) for over five years.The patient continued to have symptomatic racing of the heart, whichrequired him to visit hospital emergency rooms for treatment, despitevarious therapy with amiodarone and other appropriate therapy, anddespite prior ablation procedures for AF. Given the severity of hissymptoms, the patient therefore elected to return to theelectrophysiology laboratory for further evaluation and ablation.

FIG. 11 panels 900-910 shows signals from the right and left atriaduring AF at the commencement of electrophysiologic study. It can beseen that the AF cycle length (time between successive activation onsettimes) is quite short, shown as 172 ms and 165 ms for the first twocycles in the right atrium (panel 910), and varies, as is typical forAF. Notably, signals were more fractionated and disorganized in shape inthe left atrium (‘post LA’) and coronary sinus (‘CSP’ proximal coronarysinus; ‘CSD’ distal coronary sinus) than in the right atrium (‘HRA’ highright atrium; ‘Lat RA’ lateral right atrium; ‘post RA’ posterior rightatrium), as is common

These findings would normally guide ablation towards the left atrium. Atypical procedure in this case would commence by ablating near thepulmonary veins and confirming isolation, followed by additionalablation selecting at sites including: (a) left atrial sites offractionated electrograms, linear ablation at the roof, linear ablationat the mitral annulus, other linear ablation, then (b) right atrialablation including sites of fractionation and the cavotricuspid isthmus.This proposed procedure would take approximately 2-3 hours with a <50%chance of terminating AF, meaning that electrical cardioversion would berequired to restore normal rhythm at the conclusion of the procedure(Calkins, Brugada et al. 2007).

Rather than use this known approach, an embodiment of the method andtreatment of the present invention was applied. A catheter assemblycontaining 64 sensors (electrodes) was inserted via the femoral veinsinto the right atrium, and across a trans-septal puncture into the leftatrium of the patient. These were connected via wire cables to arecording system for collecting signals at each sensor during AF. Thesesignals were converted to digital form, and input into a computerprogram. Activation onset times were recorded for 2 seconds of AF ateach sensor. While two seconds was used with this patient, any greateror lesser periods of time may be useful. Desirably, one second or lessmay be used. In some embodiments, milliseconds may be used. Activationonset times at each sensor location were sequentially ordered in time.Stored action potential tracings were used to create an electrograph(voltage-time series), by inserting said tracings at the activation timeonsets for each sensor. Finally, a direct phase assignment technique wasused to identify a core region. An activation trail is directlyindicated by the relationship of these activation sequences to a coreregion—if they revolve around a core, then an electrical rotor isdetected and considered to be a cause, but if they emanate radially froma core region, then a focal beat is detected and considered a cause.Results were displayed as an animation on a computer monitor forphysician review.

The activation trail (panel 1035 in FIG. 12) revealed an electricalrotor as the cause for this man's AF. In FIG. 12 panel 1000, activationonset times can been seen to revolve around a core region in the rightatrium at times gray-scale and alphabetically-coded from 10 ms (level“a”) to 200 ms (level “f”) (panel 1010). No localized cause was found inthe left atrium (panel 1020). Panel 1040 displays this same rotor in adifferent form, as three snapshots in time of tissue that is depolarized(activated; “red”) and repolarized (not activated, “blue”). Viewedchronologically (from left to right), these snapshots also traceactivation sequences revolving around a core region (a rotor). This coreregion had a high likelihood of being a cause, since it was a solitarysource that controlled electrical activation for almost all of thesurrounding atrium (large space constant).

Clinically, it was surprising that this electrical rotor lay in theright atrium. The right atrial rotor site neither showed high spectraldominant frequency, nor low amplitude fractionated signals, and wouldnot normally be identified or targeted for ablation.

Ablation commenced directly at the rotor core in the right atrium (panel1050), at a site indicated by the darker dot in FIG. 12 panel 1060.Notably, AF slowed within 30 seconds of energy delivery to a cyclelength of 227 ms. Subsequent ablation at immediately adjacent sites,indicated by white dots in FIG. 10 panel 1050, further slowed AF untilit terminated to sinus rhythm within 6 minutes' ablation as shown inFIG. 13. In FIG. 13, panels 1100 to 1120, AF can be seen to stop (panel1110), followed by the restoration of normal sinus rhythm (labeled1120). At this point, AF could not be restarted using the typicaltechnique of rapid pacing as shown in FIG. 14, where panel 1210 showsrapid pacing with capture of the atrium, panel 1220 shows no inductionof AF and panel 1230 shows sinus rhythm after the end of pacing.

This result is paradigm-shifting compared to the currentstate-of-the-art, where slowing of AF typically occurs after lengthyablation that is widely and empirically applied (to 30-40% of theatrium), yet termination of persistent AF is still uncommon Conversely,we acutely slowed and acutely terminated AF with ablation of less thanapproximately 2-3% of the atrium. Ablating only at one site identified apriori in persistent AF, and seeing immediate slowing and termination ofAF is not known to have been performed previously.

Other Examples of Identification and Localization of Sources for AF

A 77 year old man presented for ablation of atrial fibrillation (AF).His history was notable for paroxysmal AF despite multipleantiarrhythmic medications, a slightly enlarged left atrium (diameter 45mm) and normal left ventricular ejection fraction (58%). At invasiveelectrophysiology study, catheters were inserted into the atria asdescribed. The invention was applied to multiple sensors. In FIG. 15panel 900 shows a localized source in the form of an electrical rotornear the left inferior pulmonary vein. Inspection of panels from left toright (forwards in time) shows that the depolarized (activated) tissuein warmer colors (red) revolves clockwise around a core region on themedial lip of the left inferior pulmonary vein (see outline as blackhourglass). Ablation at this site terminated AF acutely.

A 40 year old patient with persistent AF presented for ablation. The AFwas resistant to flecainide and other anti-arrhythmic medications, hisleft atrial diameter was 52 mm and left ventricular ejection fractionwas 69%. At invasive electrophysiology study, catheters were insertedinto the atria as described above. The invention was applied to multiplesensors. FIG. 15 panel 910 shows a localized source in the form of anelectrical rotor in the posterior wall of the left atrium. Again,viewing panels from left to right shows that activated (depolarized)tissue revolves counter-clockwise around a core region on the posteriorwall of the left atrium between the pulmonary veins. After ablation atthis site, the patient remains free of AF.

A 56 year old patient with paroxysmal AF and significant symptomspresented for ablation. The AF continued despite several anti-arrhythmicmedications. His left atrium was moderately enlarged. At invasiveelectrophysiology study, catheters were inserted into the atria asdescribed above. The invention was applied to multiple sensors. FIG. 16panel 1610 shows the output of a localized source in the left atrium,between the pulmonary veins although not lying at these veins. Thesource was repetitive (panel 1620). In panel 1630, the activation trail(1630) shows activation emanating radially from this site. In panel1640, left atrial activation is seen to be fibrillatory (disorganized).Ablation was applied to this focal beat cause, and AF terminatedacutely. At the time of filing, the patient has been completely freefrom AF for several months. This is a paradigm shifting because normalablation lesions in this patient, that circle the pulmonary veins, wouldhave missed this source. Thus, this patient would likely have been onewho would have recurred after ablation, if the prior art knowntechniques of treating AF were used.

FIGS. 17A-17C illustrate a method of reconstructing cardiac signalsassociated with a complex rhythm disorder received over a plurality ofchannels from a patient's heart. The cardiac signals can beelectrocardiogram (ECG) signals, signals from inside the heart(electrograms), representations of these signals, includingmagnetocardiogram signals or representations of mechanical activity(echo-cardiography, with or without Doppler), or generally any signalsthat represent the patient's biological rhythms. The cardiac signals canbe received and recorded on a storage medium. The signals are capturedby a plurality of sensors from the patient's heart and transmitted viathe channels to at least one computing device. The at least onecomputing device is configured to reconstruct the cardiac signals inaccordance with FIGS. 17A-17C. FIGS. 17A-17C also illustrate aconstituent method of determining an activation time of a beat in thecomplex rhythm disorder. The at least one computing device is furtherconfigured to determine the activation time of the beat in accordancewith FIGS. 17A-17C.

FIG. 17A illustrates a flowchart of an example method to classify theplurality of channels according to quality of beats in signals receivedover the channels. The method starts at operation 100A in which achannel is selected from the plurality of the channels. A signal (orpart thereof) received over the channel is retrieved. At operation 105A,one or more filters are applied to the remove baseline wander and noisefrom the signal. Additional filtering of the signal can be performed,such as, frequency domain filtering (e.g., bandpass, high-pass,low-pass, and/or other frequency domain filtering) and time-domainfiltering (e.g., median-beat filtering, template-matching to producecorrelation filtering, and/or other time-domain filtering). At operation110A, a portion of the received signal is identified or selected as ahigh-confidence level representation of a beat (e.g., template beat).For example, the template beat can be selected (algorithmically, from adatabase, or via user interaction) with one or more attributes includingbut not limited to: an acceptable amplitude (signal to noise ratio>1),an acceptable cycle length (greater than the expected rate-relatedaction potential duration), and absence of identifiable noise that maydistort its signal shape. The selected template beat is used to identifyother high-confidence beats in the signal. In one embodiment, thetemplate beat can be selected using an expert system 115A from a libraryof beat types according to one more criteria associated with the patientor the signal. These criteria include, but are not limited to age,gender, AF type (paroxysmal or persistent), length of AF history, AFcycle length, signal amplitude, recording location within the atria(e.g., left atrial, right atrial, coronary sinus), left ventricularejection fraction.

At operation 120A, successive beats are identified in the signal, suchas by performing template matching using the selected template beat.Alternate methods of identifying beats in the signal may also be used,including voltage above a threshold or maximum rate of change of voltage(first derivative, dV/dt) exceeding a threshold. At operation 125A, adetermination is made as to whether the selected signal has anacceptable signal-to-noise ratio (SNR). The SNR is generally greaterthan one (1) (i.e., the signal is larger than the noise floor) but canvary depending upon sensor location and nature of the noise. Forexample, if the signal and noise are periodic but with differentperiods, then each may be separated by their different spectralcharacteristics. If it is determined at operation 125A that the SNR ofthe signal is not acceptable, the channel is marked as anon-interpretable or non-usable channel at operation 130A.Alternatively, if it is determined at operation 125A that the SNR of thesignal is acceptable, the example method continues with operations135A-175A to classify the channel as a high-confidence channel orlow-confidence channel according to the beats in the signal associatedwith this channel.

At operation 135A, an identified beat is selected from the plurality ofidentified beats in the signal of the selected channel. At operation140A, a determination is made whether the selected beat includesmultiple components that could represent an activation onset (e.g.,deflections), one of which can be selected as the activation onset ofthe selected beat. If it is determined at operation 140A that theselected beat has multiple components, then at operation 145A theselected beat is tagged as a “Class-B” beat and an activation onset isselected in association with a component of the selected beat. A Class-Bbeat is one in which the activation onset cannot be determined with ahigh-degree of confidence, as opposed to a “Class-A” beat, which istypically monophasic (i.e., a non-complex beat in which the activationonset is not in question) in a setting of low noise and thus considereda beat having a high-degree of confidence.

Activation onset is selected based on at least one of the following:maximum dV/dt of the selected beat; template match of the beat to atemplate (selected automatically, or from a database based on patienttype and location within the heart, or interactively by the user);amplitude of the selected beat; a comparison of the components in theselected beat to components of corresponding beats on adjacent channels;and/or another one or more selection criteria. Thereafter, the methodcontinues at operation 150A described hereinbelow. Alternatively, if itis determined at operation 140A that the selected beat does not havemultiple components that could represent activation onset (e.g., Class-Abeat, as defined above (typically, a monophasic beat in an area of lownoise), an activation onset is then selected and the method alsocontinues at operation 150A as described hereinbelow.

At operation 150A, a determination is made as to whether the cyclelength of the selected beat based upon the selected activation onset isacceptable. An acceptable cycle length extending from the selectedactivation onset is defined as ranging from the minimum (rate-relatedaction potential duration, APD) to the maximum (defined cycle length,CL). For example, in FIG. 19C, the deflections 608A are not acceptablesince they fall within the minimum rate-related APD starting from thatactivation onset (depicted by 606A). The maximum CL is a measurement oftime from the selected activation onset to the next beat. From theobservations of the inventor, the minimum rate-related APD can rangefrom 90 to 400 ms. The maximum CL can also range from about 90 ms to 400ms. If at operation 150A it is determined that the cycle length isacceptable the selected beat is tagged as a “Class-A” beat at operation153.

However, if at operation 150A the determined cycle length is notacceptable, then at operations 156A, 158A, the components (defections)of the selected beat are iterated for a predetermined number ofiterations (e.g., 2 iterations) until the cycle length extending fromthe activation onset of a selected component is determined to beacceptable at operation 150A. Beats that are considered to be “Class-A”(from operation 140A) are not typically modified, that is, theiractivation onset is not altered by these operations. Thereafter, atoperation 160A a next beat is selected from the selected signal and theoperations 135A-160A are repeated for the selected beat, until no beatsremain on the selected signal (or for a predetermined number of examinedbeats).

At operation 165A, a determination is made as to whether “Class-A” beatsmake up a predetermined percentage of a total number of beats or numberof beats examined in the signal of the selected channel. Thepredetermined percentage can be selected to be 75% of the total beats orexamined beats. It is noted that other predetermined percentages can beused. If it is determined that there is a sufficient number of Class-Abeats at operation 165A, then at operation 170A, the selected channel isclassified as high-confidence channel. Alternatively, if it isdetermined that there is not a sufficient number of Class-A beats atoperation 165A, then at operation 175A, the selected channel isclassified as low-confidence channel. The method continues at operation180A, where the next channel from the plurality of channels is selectedand the operations 100A-175A are repeated for this selected channeluntil the plurality of channels have been classified in accordance withthe example method illustrated in FIG. 17A.

FIG. 17B illustrates a flowchart of an example method to revise orupdate selected activation onsets of certain quality beats in signalsreceived over the channels. Specifically, the method of FIG. 17Biterates over Class-B beats of the plurality of channels to potentiallyrevise or update selected activation onsets. Accordingly, the methodstarts at operation 200A in which a channel is selected and a Class-Bbeat is selected in the selected channel. Once Class-B beats areprocessed on the selected channel, the next channel having class-B beatsis selected until Class-B beats of the plurality of channels areprocessed (excluding channels marked as non-interpretable in operation130A of FIG. 17A).

At operations 210A, a determination is made as to whether there areClass-A beats that correspond to the selected Class-B beat (e.g., arewithin a predetermined time of the Class-B beat) in channels that areadjacent to the selected channel. If at operation 210A it is determinedthat there are corresponding Class-A beats in the signals of adjacentchannels, the method continues with operations 220A-240A. Alternatively,if at operation 210A it is determined that there is no correspondingClass-A beat in the signals of adjacent channels, the method continuesat operation 250A, as described below.

At operation 220A, a vector is computed using activation onsets of thecorresponding (nearby) Class-A beats to guide selection of activationonset at the selected Class-B beat. At operation 230A, the computedvector is refined based on at least one property. The computed vector isdefined by channel locations surrounding the channel of interest. Asshown in FIG. 19B, activation onsets are defined for the beat underconsideration in each of these channels. These activation onsets areused to define a set of plausible vectors as shown in FIG. 19D (knowingthe spatial location of each channel). The vector based upon thesesurrounding channel locations will allow the best activation onset timeto be determined for the channel of interest for that beat (e.g. FIGS.19D, 21A, 22A-22C). The vector can also be refined based on the shape orpolarity change of the selected beat, or whether activation from thissite is rotational (i.e., a rotor) or radial (i.e., a focal beat) whichboth give zero vectors at the selected Class-B beat), and/or one or moreother properties. Clearly, this vector may vary from beat-to-beat(cycle-to-cycle).

At operation 240A, a time interval (i.e., acceptance window) is definedfor the selected Class-B beat. The time interval indicates the earliestpermissible onset of the selected Class-B beat (relative to a priorbeat) and the latest permissible onset the selected Class-B beat (basedupon at least one property). The properties considered or used includethe vector, APD restitution, conduction velocity (CV) restitution,diastolic interval (DI), fiber angles, one or more anatomical factors,as well as one or more additional properties. Specifically, the inventorhas recorded conduction velocity measurements at various atrial regionsat various rates in different patient types; these conduction velocitydynamics can be use to determine if a proposed signal deflection occurstoo early or too late to be conducted along the computed vector.Similarly, the inventor has recorded measurements of action potentialduration rate-dynamics, based upon fiber angle orientations at multipleatrial locations, as well as anatomic factors (such as the knownpropensity for regions such as the crista terminalis to show conductionblock).

In one embodiment, the properties can be provided via an expert system245A from a library of properties according to one more criteriaassociated with the patient (e.g., whether the patient has advanced ageor a very large atrium, both of which predict slower conduction) or thesignal (e.g., if the signals are relatively simple or more complex).Parameters that are considered in the expert system 245A include age,gender, whether AF is paroxysmal or persistent, blood pressure, atrialvolume, left ventricular ejection fraction, presence of diabetesmellitus, and one or more other criteria. The use of DI to define anacceptance window is described in greater detail hereinbelow.

At operation 250A, the previously selected activation onset of theselected Class-B beat is revised or updated by comparison againstactivation onsets of selected components (deflections) of the signal ofthe Class-B beat that are within the acceptance window. In oneembodiment, a component that is closest to the computed vector throughthe selected Class-B beat can be selected. In another embodiment, anexpert system 255A, which stores a library of signal shapes according toone more criteria associated with the patient or the signal, can be usedto select a component of the selected Class-B beat within the acceptancewindow. For example, age, gender and one or more other criteria can beused to classify the signal shapes in the expert system 255A. Thus, theacceptance window can be defined per beat, based on rate, location,patient demographics and/or one or more other factors.

At operation 260A, a determination is made as to whether at least twoClass-A beats exist on the selected channel. If it is determined atoperation 260A that at least two Class-A beats exist on the selectedchannel, then the method continues at operation 265A to determine acycle length time interval between the Class-A beats (e.g., bysubtracting the activation onset time of the Class-A beats). Atoperation 270A, the determined time interval is successively advancedalong the signal of the selected channel to determine whether adeflection of the signal lies at or close to this time interval withinthe acceptance window. In one embodiment, the time interval can beaveraged (or median used) based on successive Class-A beats, ifavailable in the signal of the selected channel. However, if it isdetermined at operation 260A that no Class-A beat exists on the selectedchannel, then the method continues at operation 290A.

At operation 280A, the revised or updated activation onset of theselected Class-B beat is reconciled with the second activation onset ofthe determined time interval and assigned a reconciled activation onset.In one embodiment, a deflection (within the acceptance window) that isclosest to the average of these onsets can be selected as the reconciledactivation onset. Other embodiments can use the deflection closest toone of these activation times (weighted in order of importance), orother outputs from operations 145A, 250A or 270A.

At operation 290A, a next Class-B beat is selected from the signal ofthe selected channel and the method iterates through operations200A-290A for the next Class-B beat. Once Class-B beats are processed onthe selected channel, the next channel having class-B beats is selecteduntil Class-B beats of the plurality of channels are processed inaccordance with FIG. 17B, excluding non-interpretable channels marked inFIG. 17A.

FIG. 17C illustrates a flowchart of an example method to select finalactivation onsets of all beats in signals received over the plurality ofchannels. Specifically, the method of FIG. 17C iterates over Class-A andClass-B beats over the plurality of channels (high-confidence andlow-confidence channels, excluding non-interpretable channels marked ofFIG. 17A) to finalize activation onsets associated with the beats.Accordingly, the method starts at operation 300 in which a channel isselected. At operation 310A, a beat is selected in the selected channel.

At operation 320A, a vector is computed through the selected beat and anacceptance window is defined for the selected beat, as described inoperations 220A and 240A of FIG. 17B, respectively. The operations ofFIG. 17C differ from the previous operations in that vectors can now becomputed from Class-A beats and Class-B beats (as revised in FIG. 17B).The purpose of is to ensure that activation onsets are consistentbetween all Class-A beats and Class-B beats. Final adjustment ofactivation onsets can be made to minimize inconsistencies that nowarise. In one embodiment, an expert system 325A can be used to provideone or more properties to define the acceptance window, such as APD andCV restitution, DI, and/or other properties. At operation 330A, thecomputed vector is refined based on at least one property. For example,the computed vector can be refined based on wavefront curvature whenmapped onto the atrium, beat signal shape, known anatomic factors suchas conduction block at the crista terminalis, presumed fiber anglesand/or one or more other properties. In one embodiment, these factorsare quantified and coded in an expert system 335A, based upon patientage, gender, whether AF is paroxysmal or persistent, blood pressure,atrial volume, left ventricular ejection fraction, presence of diabetesmellitus, and one or more other criteria. At operation 338A, activationonset is determined for the selected beat within the acceptance windowwhere the vector crosses the selected beat.

At operation 340A a determination is made as to whether the previousactivation onset of the selected beat (from FIG. 17B) is approximatelyequivalent (e.g., within a predetermined threshold) to the currentlydetermined activation onset of the selected beat. If it is determined atoperation 340A that the previous activation onset of the selected beatis approximately equivalent, then the method continues at operation 370Abelow. Alternatively, if it is determined at operation 340A that theprevious activation onset of the selected beat is not approximatelyequivalent, the method continues at operation 350A.

At operation 350A, the previous activation onset is reconciled with thecurrent activation onset to obtain a reconciled activation onset. In oneembodiment, a deflection (within the acceptance window) that is closestto the average of these activation onsets can be selected as thereconciled activation onset. An expert system 355A can be used toprovide cycle length estimates, which can be used to estimate theposition of each activation onset following a specific beat, with theassumption in this case that signals demonstrate regularity at thischannel. At operation 360A, a determination is made as to whetherreconciliation of activation onsets was required. If at operation 360Athe reconciliation was required, then at operation 363A, the tagging ofthe selected beat is updated to a Class-B beat. However, if at operation360A the reconciliation was not required, then at operation 368A, thetagging of the selected beat is updated to a Class-A beat.

After operations 363A and 368A, the method continues at operation 370Ain which the reconciled activation onset, determined activation onset(from operation 338A), or existing activation onset (from operation 280Aor as described with reference to operations 140A and 153A for class Abeats) is selected as the final activation onset for the selected beat.At operation 380, a next beat is selected on the selected channel andoperations 320A-370A are iterated for the selected beat until all beatsare processed on the selected channel. Once all beats are processed onthe selected channel, a next channel is selected at operation 390A andoperations 310A-380A are iterated for the selected channel until allchannels are processed in accordance with FIG. 17C, excludingnon-interpretable channels marked in FIG. 17A.

The diastolic interval (DI) and action potential duration (APD)relationship can be used to identify activation onsets in a beat of asignal. In complex rhythm disorders (e.g., cardiac fibrillation), when asignal quality is insufficient to accurately determine an activationonset of a Class-B beat in a signal received over a channel, activationonset of a Class-A beat in the signal can be used along with the APDdependence on a previous DI to estimate an acceptance window for theClass-B beat. More specifically, an APD can be defined for eachactivation cycle based on a previous DI to reconstruct an actionpotential (AP) trace from the signal.

An AP reconstruction attempt is deemed to have failed when any definedAPD is less than a predefined minimum (e.g., 90 ms) or exceeds theavailable cycle length (CL) within which the APD must fit. The AP traceshown in FIG. 18 illustrates such a failure.

For example, considering the dashed lines to be selected activationonsets and the curved vertical lines to be an APDs in the APreconstruction, the fifth APD has not fallen to an acceptable level forreactivation before the next activation onset is reached. This is deemeda reconstruction failure and implies that the APD-DI relationship used,paired with the initial DI used to calculate the first APD (DI seed) isnot valid for representing the real APDs. It could be that the APD-DIrelationship was incorrect, the DI seed was incorrect, or both.

If the relationship between DIs and the following APDs is known, then apatient-specific restitution curve can be used to check a series ofselected activation onsets without performing a number of calculationsthrough a range of values for the constants in the DI-APD relationship.In accordance with patient specific restitution curve, a series ofactivation onsets is considered incorrect if there are no DI seeds thatresult in a correctly reconstructed AP trace. When reconstructing the APtrace, if a disproportionately high number of reconstruction attempts(for each DI seed) fails for any low confidence activation onset (afterthe first four activation onsets), that activation onset is deemedincorrect and should be re-evaluated.

A linear or logarithmic function (algorithm) can be used to relate DIand APD. For example, the linear function can be APD=C1*DI+C2. Thelogarithmic function can be APD=C₁*ln(DI)+C₂. If the constants in therelation between DI and APD are unknown, the linear functionAPD=C1*DI+C2 can be assumed. AP reconstructions can be performed forplausible DI seeds and for plausible constants C1 and C2. The totalnumber of AP reconstruction failures can be tracked for each activationonset that is marked. A largest number of failures in AP reconstructionare expected to occur in the first few activation onsets, as theincorrect DI seeds and constants will usually fail to fit the sequencewithin the first few activation onsets. If a disproportionately largenumber of failures occur later in the AP reconstruction, then theactivation onset is considered “implausible” and marked for reviewand/or further analysis.

If an assumption is made that the relation between DI an APD isinvariant for all locations in the heart, then the accuracy of thecalculation can be improved by excluding constants C1 and C2 that leadto failed trace reconstructions in signals that have high confidenceactivation onsets. In this way, the foregoing algorithm will exclude allmathematical DI-APD relationships that are not likely to apply to thespecific patient being analyzed.

FIG. 19A shows a plurality of time-varying signals 404 obtained fromsensors receiving cardiac (electrical) activity from a patient's heartduring a complex rhythm disorder. The sensors can be included in acardiac catheter that is introduced inside the patient or the sensorscan be disposed outside the patient. Each of the signals is representedby a signal identifier, such as “A1A2”, “B3B4”, and “B5B6”. An examplesnapshot or window 402A, which is indicated by a shaded portion in FIG.19A, represents example activity on each of twelve (12) of the cardiacsignals 404A within a specified time period (e.g., 2 ms). The cardiacsignals 402A represent electrical cardiac activity, during a complexrhythm disorder such as atrial fibrillation (AF), of various locationsin the atrium, at which a corresponding sensor is located. It is to benoted that the detection of the “earliest” activation onset isimpossible through mere visual inspection of the cardiac signals 404Ashown in FIG. 19A, as there is no discernable quiescent period in thecardiac signals 404A to enable detection of the earliest activationonset from the signals 404A.

FIG. 19B shows just that portion of electrical activity within thewindow 402A shown in FIG. 19A. The vertical lines 504A representactivation onsets for each of the time-varying cardiac signals. As canreadily be seen from the cardiac signals shown in FIG. 19B, theactivation onsets 504A for at least the signals identified by C5C6,C7C8, and D7D8 are not well-defined. Arrows 512A define a vector thatconnects corresponding points in adjacent time-varying cardiac signals.As can be seen there is no discernable earliest activation onset in thesignals shown in FIG. 19B. In other words, it is not possible to simplytrace activation back to the earliest channel (that, in this example, ischannel C7C8). This is because multiple co-existing waves may exist inAF (unlike rhythms such as supraventricular tachycardia). FIG. 19D showssome of these potential wave directions, indicating multiple potentialwavepaths. Considerations of maximum and minimum potential conductionvelocity, and other physiological properties above, will determine thewave paths that are more or less likely to explain the observedcontinuous, varying, and complex signals at each electrode.

FIG. 19C shows an expanded view of the signal identified by C7C8 forwhich an activation onset cannot be determined due to multipledeflections, and an indication of the corresponding rate-adjustedactivation potential duration (APD) item 606. The rate-adjusted APD 606indicates that signals at this particular channel C7C8 cannot occuruntil near the end of the rate-adjusted APD 606A. This fact is used toeliminate deflections of signal C7C8 that occur within the APD 606A, asshown by arrows 608A, and avoid counting the defections as activationonsets. This is because the cardiac tissue is unable to physicallyreactivate for the duration of the APD (“repolarization”) 606A.Naturally, the actual position of the APD 606A depends on the timing ofthe prior activation onset time 610A.

FIG. 19D is a two-dimensional representation of the positions of thecardiac sensors or electrodes, which provides a grid on the patient'satrium. The representation of points on the grid, such as “B78”, “C56”,and “D12”, correspond to the electrodes or sensors that are used toprovide the corresponding time-varying cardiac signals, such as “B7B8”,“C5C6”, and “D1D2”, respectively, as shown in FIGS. 19A and 19B. Thus,sensor “B78” corresponds to time-varying cardiac signal “B7B8”, andsensor “C56” corresponds to cardiac signal “C5C6”. Arrows 714Aconnecting specified sensors in FIG. 19D represent the vector directedbetween the corresponding locations of the patient's atrium. Thus, usingonly information in the cardiac signal C5C6, the activation onsetassociated with signal C5C6 can be determined using non-linearinterpolation of the vector from sensors C78 to C34, the activations forwhich are both known. Alternative vectors, such as that from sensors B34to C34 are unlikely, since they require a conduction velocity that istoo rapid to be exhibited by the cardiac tissue. Cardiac signal D7D8 istypically discarded as an un-interpretable channel or signal.

FIG. 20A shows examples of various methods for detecting beats,determining activation onsets, and disregarding noise on thetime-varying cardiac signals shown in FIG. 19A. A time-varying cardiacsignal from a high-confidence channel is shown as signal 802A. In orderto mark or tag the activation onsets in signal 802A, a template 804A canbe derived from one of the more discernible deflections (or beats) in agiven time period of the signal 802A. This template 804A can then usedto detect subsequent and prior beats in signal 802A by using correlationfunctions, or other methods. Another method that can be used to tagactivation onsets in signal 802A is shown using a rate-adapted APD 806A,which was essentially described above in reference to FIG. 19C. That is,any deflections that occur in signal 802 before the end 808A of the APD806A, are eliminated or classified as noise since the heart tissue isphysically unable to reactivate during this time. Accordingly, thedeflections pointed to by arrow 810 are eliminated from being consideredactivation onsets. Yet another method of accurately determiningactivation onsets is by filtering out noise within a specified frequencyrange or bandwidth, as shown by arrows 812A in FIG. 20A, which is thenalso eliminated from consideration as activation onsets. Activationonset times are determined using a combination of template match,crossing a predetermined voltage threshold, and a maximum dV/dt, whichis defined as the maximum rate of change of the voltage with respect totime or slope of the time-varying cardiac signal.

FIG. 20B shows a signal 902A from a low-confidence channel. Forlow-confidence channels, different templates may be used to detectvarious shapes of signal components or deflections. Thus, a differenttemplate could be defined and used to detect activation onsetsassociated with each of a plurality of different shapes identified by“A”, “B”, and “C” in FIG. 20B.

FIG. 20C shows a signal 1010A from a complex channel, in which theshapes of the individual beat representations vary widely from beat tobeat. The vector and APD restitution methods, are among the methodsdescribed above, which may be used to determine activation onsets forthis type of signal.

FIGS. 21A and 21B provide additional details to those shown in FIGS. 19Band 19D, respectively, to define a method of determining activationonsets for class B-beats using vectors. As in FIG. 19B, the shortvertical lines 1102A shown in FIG. 21A represent example activationonsets determined with respect to the time-varying cardiac signals. Thenumbers 1104A noted in proximity to each of the vertical lines representthe time of the activation onsets for the corresponding time-varyingcardiac signal relative to a given starting time. For example, theactivation onset time for cardiac signal B3B4, which is provided as“37”, occurs before the activation onset time for cardiac signal B1B2,which is provided as “42”. FIG. 21B shows the matrix or grid of sensorsdenoted by identifications 1106, such as “B34”, “B12”, “C12”, and “D12”.Likely vectors are shown in FIG. 21B as arrows or lines 1108A thatconnect specific sensors 1106A. For example, assume that the activationonset at cardiac signal C5C6, which is denoted as a B-channel, is to bedetermined using vectors from surrounding channels having determinateactivation onsets. From FIG. 21B, the most likely vector paths throughcardiac signal C5C6 (with the unknown activation onset) is from sensorC78 to C34 since alternate paths through, such as through sensor C56,would exhibit a conduction velocity that is either too fast (such asfrom sensor B56 to C56), or less probable (such as a zigzag progressionthrough sensors B78, B56, C78, and C56) than that from sensors C78 toC34. Accordingly, the outcome of the analysis indicates that theactivation onset for the cardiac signal C5C6 is derived using a vector,which is not necessarily linear, between the activation onsetsassociated with sensors C78 and C34, and thus cardiac signals C7C8 andC3C4, respectively.

FIGS. 22A-22C show displays of the reconstructed wave paths infibrillation from selected activation onsets according to the method andsystems described in this application. The activation onsets areprovided as numbers (in units of milliseconds) arranged in atwo-dimensional array or grid. The grid shown in each of FIGS. 22A-22Cof numbers corresponds to the grid of cardiac sensors shown in FIGS.19B, 19D, and 21B, and thus represents activation onset times determinedby corresponding cardiac sensors at the same location. For each channel,the beat under consideration is provided with a number representing itsactivation onset time in milliseconds, and hence the resultingactivation vector over this two-dimensional space. It is to be notedthat these activation times may indicate class-A beats, or also class-Bbeats after initial assignment from FIG. 17B. Low-confidence channelsare indicated by a question mark. The wave paths are reconstructed asspatial contours of the same or similar activation onsets. For example,in FIG. 22A, a contour line 1302A is drawn connecting two sensors withvery similar activation onsets (12 ms and 11 ms) to represent a locationof the wavefront at approximately 11 ms to 12 ms. Similarly, contourline 1304A is drawn to connect sensors associated with similaractivation onset times (90 ms, 81 ms, and 81 ms) to represent a locationof the wavefront at approximately 81 ms to 90 ms. Each of the contourlines is marked to indicate the relative time of each contour line withrespect to the remaining contour lines. Accordingly, the earliestcontour line will be indicated with “

”, and the latest contour line, identified as line 1306A, will beindicated as “

”. Arrows 1310A, 1312A indicate the direction of the vector as the wavepropagates across the contour lines. Thus, FIG. 22A shows a collision oftwo separate vectors 1310A, 1312A. The contour lines and vectors areused to define activation onsets at the low confidence signals markedwith a question mark.

In addition, activation onsets are determined using APD restitution andrepolarization times as well as fiber angles (anatomic paths). Forinstance, if fiber angles are perpendicular to the vector of propagationat the indicated collision, this adds confidence to the results.Otherwise, another iteration may be required to ensure that activationonset times were not skewed by particular deflections in class-Bchannels that gave this appearance of slowing. In general, it isexpected that wave propagation perpendicular to fiber angles is slowerthan propagation parallel to fiber angles. Fiber angles are providedfrom experimentation, and from known angles and anisotropy at certainlocations in the atrium, such as the posterior left atrial wall and theseptopulmonary bundle of Papez.

Beat shape changes or path discontinuities are shown as blue lines. Ingeneral, it is considered that inversion of the beat signal polarityindicates that the wave is passing the bipolar recording electrode inthe opposite direction. This information can be used as an additionalverification step to determine if wave contours did indeed alter attimes of substantial beat polarity change.

Similarly, FIG. 22B shows another example display, except that thewavefront defined thereby is a rotor or rotational pattern, as indicatedby the progression of contour line 1402A-1412A from “

” to “

”, and an arrow 1414A.

Similarly, FIG. 22C shows an example display that represents a focalbeat emanating from a central location defined by a contour line 1502A,which proceeds outward along the arrows 1504A towards a contour line1506A.

FIG. 23A shows a two-dimensional representation of a matrix of sensors1602A, which are shown as points or electrode positions superimposed ona cardiac atrial surface, indicated by the hand-drawn shape. This shapeindicates the left atrium, cut horizontally through the plane of themitral valve with the two halves folded up and down. Thus, the topportion indicates the superior mitral valve and the bottom portionindicates the inferior mitral valve.

FIG. 23B shows time-varying cardiac signals obtained from nine (9) ofthe cardiac electrodes or sensors 1602A shown in FIG. 23A. The cardiacsignals are denoted as raw signals 1702A, since they are obtaineddirectly, or with a minimal amount of processing or filtering, from thecardiac sensors.

FIG. 23C shows an example display obtained from the raw signals 1702Ashown in FIG. 23B using conventional methods known in the art. Since thedisplay is obtained directly from the raw signals the result is aconfusing map with a plurality of transient patterns that do notindicate any pattern indicative of the origin or earliest activationonset associated with the complex rhythm disorder (i.e. it does notindicate an activation trail). The display of FIG. 24A corresponds tothe grid shown in FIG. 23A, in that locations in the grid correspond tothe position of the sensors as they relate to locations in a cardiacvolume. The shaded areas shown in the display represent activationonsets relative to a given start time in accordance with the scale 1802Aon the right side of the display. The gray scale 1802A indicates theshading associated with activation onsets (e.g., in milliseconds). Thus,for example, those portions of the display that are shown in area 1804Ahave an earlier activation onset time than those portions shown in area1806A, which are earlier than those portions shown in area 1808A.

FIG. 23C shows the result of tagging activation onsets for beats in eachof the nine raw signals in accordance with the systems and methoddescribed herein. The activation onsets are shown as dotted lines 1902A.Processes outlined in FIGS. 17A-17C are used to generate the activationtimes for each beat in each channel indicated by vertical lines in FIG.23C.

FIG. 24B shows an example display derived from the tagging of activationonset times in FIG. 23C, in which a rotor is shown as where the red area(indicated by “R”) meets the blue area (indicated by “B”) via thedifferent shades of the gray scale between these shades, as shown byarrow 2002 around a core. This core is the fulcrum around whichactivation rotates to create a rotor. It is to be noted that the displayin FIG. 24B clearly indicates the rotor which was undetectable from thedisplay shown in FIG. 24A. It is also to be noted that the preciselocation of the rotor core may move in space (migrate) over time, buttypically remains within a small location in space (“locus”).

FIG. 23D shows a reconstruction of the activation potential duration(APD) 2102A, which starts at the activation onsets determined in FIG.23C and extends for a specified time or decay thereafter. Accordingly,the APD 2102A begins with the activation onsets 2104A and extends untilthe end 2106A of the APD.

FIG. 24C shows a display in which the tagged activation times determinedin FIG. 23C and the reconstructed APD determined in FIG. 23D, are usedto define the intersection between a depolarization line, which isindicated by a contour line 2202A, and a repolarization line, which isindicated by a contour line 2204A. Specifically, each reconstructed APDtime series is used as an input to the Hilbert transform. A detrendingalgorithm is applied to set voltages at the activation times to zero.The Hilbert transform is used to construct the phase plane of detrendedsignals. Then, the Hilbert transform at all electrodes is interpolatedacross the fine regular grid. The spatial distributions of phase areanalyzed with a topological charge technique to locate phasesingularities associated with the ends of wavefronts such as at the tipof a reentrant wave. Activation wavefronts are then constructed bytracking isolines of zero phase using an active-edge technique. Insummary, for a snapshot in time, line 2202A indicates the leading edgeof depolarization across the tissue, and line 2204A indicates thetrailing edge of repolarization. The intersection of these linesindicates the rotor core. It has been shown by clinical reduction topractice that this rotor core is the location where targeted ablationenergy may terminate and eliminate AF. Other treatments, such asdelivery of a depolarizing or repolarizing current, and delivery of genetherapy or other active agents can also be applied to the locus oftissue (spatial region) where the rotor lies.

It is to be noted that these exact techniques can also reveal a focalbeat, for which the activation time contours and Hilbert transform wouldreveal activations emanating from a focal beat origin, with subsequentdisorganization if the rhythm resulting in atrial fibrillation orventricular fibrillation (for which a treatment example is describedabove).

FIG. 25 is a block diagram of a computer system 2300A. The computersystem 2300A can include a set of instructions that can be executed tocause the computer system 2300A to perform any one or more of themethods or computer-based functions disclosed herein with respect toFIGS. 17A-24C. The computer system 2300A or any portion thereof, mayoperate as a standalone device or may be connected (e.g., using anetwork 2324A) to other computer systems or devices disclosed hereinwith respect to FIGS. 17A-24C. For example, the computer system 2300Acan include or be included within any one or more of the catheter,computing device, server, biological sensor, and/or any other devices orsystems disclosed herein with respect to FIGS. 1-24C.

In a networked deployment, the computer system 2300A may operate in thecapacity of a server or a client machine in a server-client networkenvironment, or a peer machine in a peer-to-peer (or distributed)network environment. The computer system 2300A can also be implementedas or incorporated into various devices, such as a personal computer(PC), a tablet PC, a personal digital assistant (PDA), a web appliance,a communications device, a mobile device, a server, client or any othermachine capable of executing a set of instructions (sequential orotherwise) that specify actions to be taken by that machine. Further,while a single computer system 2300A is illustrated, the term “system”shall also be taken to include any collection of systems or sub-systemsthat individually or jointly execute a set, or multiple sets, ofinstructions to perform one or more computer functions.

As illustrated in FIG. 25, the computer system 2300A can include aprocessor 2302A, e.g., a central processing unit (CPU), agraphics-processing unit (GPU), or both. Moreover, the computer system2300A can include a main memory 2304A and a static memory 2306A that cancommunicate with each other via a bus 2326A. As shown, the computersystem 2300A may further include a video display unit 2310A, such as aliquid crystal display (LCD), an organic light emitting diode (OLED), aflat panel display, a solid state display, or a cathode ray tube (CRT).Additionally, the computer system 2300A may include an input device2312A, such as a keyboard, and a cursor control device 2314A, such as amouse. The computer system 2300A can also include a disk drive unit2316A, a signal generation device 2322A, such as a speaker or remotecontrol, and a network interface device 2308A.

In a particular embodiment, as depicted in FIG. 25, the disk drive unit2316A may include a machine or computer-readable medium 2318A in whichone or more sets of instructions 2320A (e.g., software) can be embedded.Further, the instructions 2320A may embody one or more of the methods,functions or logic as described herein with reference to FIGS. 1-24C. Ina particular embodiment, the instructions 2320A may reside completely,or at least partially, within the main memory 2304A, the static memory2306A, and/or within the processor 2302A during execution by thecomputer system 2300A. The main memory 2304A and the processor 2302Aalso may include computer-readable media.

In an alternative embodiment, dedicated hardware implementations, suchas application specific integrated circuits, programmable logic arraysand other hardware devices, can be constructed to implement one or moreof the methods, functions or logic described herein. Applications thatmay include the apparatus and systems of various embodiments can broadlyinclude a variety of electronic and computer systems. One or moreembodiments described herein may implement functions using two or morespecific interconnected hardware modules or devices with related controland data signals that can be communicated between and through themodules, or as portions of an application-specific integrated circuit.Accordingly, the present system encompasses software, firmware, andhardware implementations.

In accordance with the various embodiments, the methods, functions orlogic described herein may be implemented by software programs that aretangibly embodied in a processor-readable medium and that may beexecuted by a processor. Further, in an example, non-limited embodiment,implementations can include distributed processing, component/objectdistributed processing, as well as parallel processing. Alternatively,virtual computer system processing can be constructed to implement oneor more of the methods, functionality or logic as described herein.

While the computer-readable medium is shown to be a single medium, theterm “computer-readable medium” includes a single medium or multiplemedia, such as a centralized or distributed database, and/or associatedcaches and servers that store one or more sets of instructions. The term“computer-readable medium” shall also include any medium that is capableof storing, encoding or carrying a set of instructions for execution bya processor or that cause a computer system to perform any one or moreof the methods, functions, logic or operations disclosed herein.

In a particular non-limiting, example embodiment, the computer-readablemedium can include a solid-state memory such as a memory card or otherpackage that houses one or more non-volatile read-only memories.Further, the computer-readable medium can be a random access memory orother volatile re-writable memory. Additionally, the computer-readablemedium can include a magneto-optical or optical medium, such as a diskor tapes or other storage device to capture carrier wave signals such asa signal communicated over a transmission medium. A digital fileattachment to an e-mail or other self-contained information archive orset of archives may be considered a distribution medium that isequivalent to a tangible storage medium. Accordingly, the disclosure isconsidered to include any one or more of a computer-readable medium or adistribution medium and other equivalents and successor media, in whichdata or instructions may be stored.

In accordance with various embodiments, the methods, functions or logicdescribed herein may be implemented as one or more software programsrunning on a computer processor. Dedicated hardware implementationsincluding, but not limited to, application specific integrated circuits,programmable logic arrays and other hardware devices can likewise beconstructed to implement the methods described herein. Furthermore,alternative software implementations including, but not limited to,distributed processing or component/object distributed processing,parallel processing, or virtual machine processing can also beconstructed to implement the methods, functions or logic describedherein.

It should also be noted that software which implements the disclosedmethods, functions or logic may optionally be stored on a tangiblestorage medium, such as: a magnetic medium, such as a disk or tape; amagneto-optical or optical medium, such as a disk; or a solid statemedium, such as a memory card or other package that houses one or moreread-only (non-volatile) memories, random access memories, or otherre-writable (volatile) memories. A digital file attachment to e-mail orother self-contained information archive or set of archives isconsidered a distribution medium equivalent to a tangible storagemedium. Accordingly, the disclosure is considered to include a tangiblestorage medium or distribution medium as listed herein, and otherequivalents and successor media, in which the software implementationsherein may be stored.

Thus, methods, systems and apparatuses for detection, diagnosis andtreatment of biological (complex) rhythm disorders have been described.Although specific example embodiments have been described, it will beevident that various modifications and changes may be made to theseembodiments without departing from the broader scope of the inventivesubject matter described (invention) herein. Accordingly, thespecification and drawings are to be regarded in an illustrative ratherthan a restrictive sense. The accompanying drawings that form a parthereof, show by way of illustration, and not of limitation, specificembodiments in which the subject matter may be practiced. Theembodiments illustrated are described in sufficient detail to enablethose skilled in the art to practice the teachings disclosed herein.Other embodiments may be utilized and derived therefrom, such thatstructural and logical substitutions and changes may be made withoutdeparting from the scope of this disclosure. This Detailed Description,therefore, is not to be taken in a limiting sense, and the scope ofvarious embodiments is defined only by the appended claims, along withthe full range of equivalents to which such claims are entitled.

Such embodiments of the inventive subject matter may be referred toherein, individually and/or collectively, by the term “invention” merelyfor convenience and without intending to voluntarily limit the scope ofthis application to any single invention or inventive concept if morethan one is in fact disclosed. Thus, although specific embodiments havebeen illustrated and described herein, it should be appreciated that anyarrangement calculated to achieve the same purpose may be substitutedfor the specific embodiments shown. This disclosure is intended to coverany and all adaptations or variations of various embodiments.Combinations of the above embodiments, and other embodiments notspecifically described herein, will be apparent to those of skill in theart upon reviewing the above description.

In the foregoing description of the embodiments, various features aregrouped together in a single embodiment for the purpose of streamliningthe disclosure. This method of disclosure is not to be interpreted asreflecting that the claimed embodiments have more features than areexpressly recited in each claim. Rather, as the following claimsreflect, inventive subject matter lies in less than all features of asingle disclosed embodiment. Thus the following claims are herebyincorporated into the Description of the Embodiments, with each claimstanding on its own as a separate example embodiment.

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The invention claimed is:
 1. A system to reconstruct cardiac informationassociated with a patient's heart to indicate a source of the complexrhythm disorder, the system comprising: at least one computing deviceconfigured to: receive cardiac information signals from a plurality ofsensors spatially related with the patient's heart during the complexrhythm disorder, classify the cardiac information signals into high andlow confidence signals, wherein the high and low confidence signals areseparated by a confidence threshold, the confidence threshold associatedwith a predetermined percentage of beats having discernible activationonsets out of total beats associated with each of the high and lowconfidence signals; determine an activation onset associated with atleast one low confidence signal within an acceptance time window, theacceptance time window associated with the low confidence signalindicating an earliest activation and a latest termination for a beat inthe low confidence signal; order the activation onsets associated withthe high and low confidence signals to indicate the source of thecomplex rhythm disorder; and treatment means for modifying the indicatedsource.
 2. The system according to claim 1, wherein the at least onecomputing device determines activation onsets associated with the lowconfidence signals within acceptance time windows, each of theacceptance time windows associated with a low confidence signalindicating an earliest activation and a latest termination for a beat inthe low confidence signal.
 3. The system according to claim 1, whereinthe at least one computing device orders the activation onsets based onat least one of temporal, spatial and phase information.
 4. The systemaccording to claim 1, wherein the at least one computing devicedetermines activation onsets associated with the low confidence signalsusing a time vector connecting at least two discernible activationonsets of the high confidence signals associated with spatially adjacentsensors.
 5. The system according to claim 1, wherein the complex rhythmdisorder comprises no discernible period during which the cardiacinformation signals are quiescent.
 6. The system according to claim 1,wherein the complex rhythm disorder comprises no discernible earliestactivation onset associated with the cardiac information signals.
 7. Thesystem according to claim 1, wherein the at least one computing deviceclassifies the cardiac information signals into the high and lowconfidence signals using at least one of activation onset, cycle length(CL), action potential duration (APD), and amplitude, wherein theactivation onsets are determined by using at least one of maximum dV/dt,template matching, frequency and amplitude.
 8. The system according toclaim 1, wherein the at least one computing device determines theacceptance time window using at least one of action potential duration(APD), conduction velocity (CV), fiber angle, time vector connecting atleast two discernible activation onsets of the high confidence signalsassociated with spatially adjacent sensors, and anatomic factors.
 9. Thesystem according to claim 1, wherein the at least one computing deviceremoves baseline wander and noise from the cardiac information signals,the computing device filtering the cardiac information signals.
 10. Thesystem according to claim 1, wherein the at least one computing devicedisregards at least one of the cardiac information signals using atleast one of signal-to-noise ratio (SNR), template matching, frequencyand amplitude.
 11. The system according to claim 6, wherein the at leastone computing device performs template matching by identifyinghigh-confidence beats associated with the cardiac information signals astemplates.
 12. The system according to claim 7, further comprising anexpert system, the expert system performing template matching.
 13. Thesystem according to claim 1, wherein the at least one computing deviceclassifies beats associated with the cardiac information signals basedon a shape associated with the beats.
 14. The system according to claim1, wherein the at least one computing device classifies beats associatedwith the cardiac information signals as high confidence beats inresponse to a cycle length (CL) associated with the beats being greaterthan or equal to a minimum action potential duration (APD) and less thanor equal to a maximum CL.
 15. The system according to claim 1, whereinthe at least one computing device classifies beats associated with thecardiac information signals as low confidence beats in response to acycle length (CL) associated with the beats being less than a minimumaction potential duration (APD) or greater than a maximum CL.
 16. Thesystem according to claim 4, wherein the at least one computing devicemodifies the time vector using at least one of beat shape, beatpolarity, surrounding rotating emanation and radial emanation.
 17. Thesystem according to claim 1, further comprising an expert system, theexpert system determining the acceptance time window using at least oneof action potential duration (APD), conduction velocity (CV), timevector connecting at least two discernible activation onsets of the highconfidence signals associated with spatially adjacent sensors, frequencyand fiber angle.
 18. The system according to claim 1, further comprisingan expert system, the expert system determining activation onsets usingwave shapes.
 19. The system according to claim 1, wherein the at leastone computing device determines activation onsets associated with thelow confidence signals using at least one of rolling average and phaselock.
 20. The system according to claim 1, wherein the at least onecomputing device determines activation onsets associated with the lowconfidence signals by reconciling activation onsets determined by usingat least two of the vector, acceptance window, rolling average, andphase lock.
 21. The system according to claim 1, wherein the systemfurther comprises: at least one storage device configured to store thecardiac information signals received from the patient's heart, the atleast one storage device operatively coupled to the at least onecomputing device to provide the signals to the at least one computingdevice.
 22. The system according to claim 1, wherein the system furthercomprises: a catheter comprising a plurality of sensors to receive thecardiac information signals from the patient's heart and operativelycoupled to the at least one computing device to provide the cardiacinformation signals to the at least one computing device.
 23. The systemaccording to claim 1, wherein the treatment means is selected from thegroup consisting of ablation energy, stimulation energy, drug therapy,cellular therapy and gene therapy.
 24. An assembly to facilitatereconstruction of cardiac information associated with a patient's heartto indicate a source of a complex rhythm disorder, the assemblycomprising: a catheter comprising a plurality of sensors configured toprovide cardiac information signals; and a computer-readable mediumoperatively coupled to the sensors, the computer-readable mediumcomprising instructions, which when executed by a computing device,cause the computing device to reconstruct cardiac information associatedwith a patient's heart to indicate a source of a complex rhythm disorderby: receiving cardiac information signals from a plurality of sensorsassociated spatially with the patient's heart during the complex rhythmdisorder; classifying the cardiac information signals into high and lowconfidence signals, wherein the high and low confidence signals areseparated by a confidence threshold, the confidence threshold associatedwith a predetermined percentage of beats having discernible activationonsets out of total beats associated with each of the high and lowconfidence signals; determining an activation onset associated with atleast one low confidence signals within an acceptance time window, theacceptance time window associated with the low confidence signalindicating an earliest activation and a latest termination for a beat inthe low confidence signal; ordering the activation onsets associatedwith the high and low confidence signals to indicate the source of thecomplex rhythm disorder; and treatment means for modifying the indicatedsource.
 25. The assembly according to claim 24, wherein thecomputer-readable medium comprises instructions, which when executed bya computing device, cause the computing device to determine activationonsets associated with the low confidence signals within acceptance timewindows, each of the acceptance time windows associated with a lowconfidence signal indicating an earliest activation and a latesttermination for a beat in the low confidence signal.
 26. The assemblyaccording to claim 24, wherein the computer-readable medium comprisesinstructions, which when executed by a computing device, cause thecomputing device to order the activation onsets based on at least one oftemporal, spatial and phase information.
 27. The assembly according toclaim 24, wherein the computer-readable medium comprises instructions,which when executed by a computing device, cause the computing device toreconstruct cardiac information associated with a patient's heart toindicate a source of the complex rhythm disorder by determiningactivation onsets associated with the low confidence signals using atime vector connecting at least two discernible activation onsets of thehigh confidence signals associated with spatially adjacent sensors. 28.The assembly according to claim 24, wherein the complex rhythm disordercomprises no discernible period during which the cardiac informationsignals are quiescent.
 29. The assembly according to claim 24, whereinthe complex rhythm disorder comprises no discernible earliest activationonset associated with the cardiac information signals.
 30. The assemblyaccording to claim 24, wherein the computer-readable medium comprisesinstructions, which when executed by a computing device, cause thecomputing device to reconstruct cardiac information associated with apatient's heart to indicate a source of the complex rhythm disorder byclassifying the cardiac information signals into the high and lowconfidence signals using at least one of activation onset, cycle length(CL), action potential duration (APD), and amplitude, wherein theactivation onsets are determined by using at least one of maximum dV/dt,template matching, frequency and amplitude.
 31. The assembly accordingto claim 24, wherein the computer-readable medium comprisesinstructions, which when executed by a computing device, cause thecomputing device to reconstruct cardiac information associated with apatient's heart to indicate a source of the complex rhythm disorder bydetermining the acceptance time window using at least one of actionpotential duration (APD), conduction velocity (CV), fiber angle, timevector connecting at least two discernible activation onsets of the highconfidence signals associated with spatially adjacent sensors, andanatomic factors.
 32. The assembly according to claim 24, wherein thecomputer-readable medium comprises instructions, which when executed bya computing device, cause the computing device to reconstruct cardiacinformation associated with a patient's heart to indicate a source ofthe complex rhythm disorder by: removing baseline wander and noise fromthe cardiac information signals; and filtering the cardiac informationsignals.
 33. The assembly according to claim 24, wherein thecomputer-readable medium comprises instructions, which when executed bya computing device, cause the computing device to reconstruct cardiacinformation associated with a patient's heart to indicate a source ofthe complex rhythm disorder by disregarding at least one of the cardiacinformation signals using at least one of signal-to-noise ratio (SNR),template matching, frequency and amplitude.
 34. The assembly accordingto claim 30, wherein the computer-readable medium comprisesinstructions, which when executed by a computing device, cause thecomputing device to reconstruct cardiac information associated with apatient's heart to indicate a source of the complex rhythm disorder bytemplate matching by identifying high-confidence level beats associatedwith the cardiac information signals as templates.
 35. The assemblyaccording to claim 30, wherein the computer-readable medium comprisesinstructions, which when executed by a computing device, cause thecomputing device to reconstruct cardiac information associated with apatient's heart to indicate a source of the complex rhythm disorder bytemplate matching using an expert system, the expert system using beattypes to perform template matching.
 36. The assembly according to claim24, wherein the computer-readable medium comprises instructions, whichwhen executed by a computing device, cause the computing device toreconstruct cardiac information associated with a patient's heart toindicate a source of the complex rhythm disorder by classifying beatsassociated with the cardiac information signals based on a shapeassociated with the beats.
 37. The assembly according to claim 24,wherein the computer-readable medium comprises instructions, which whenexecuted by a computing device, cause the computing device toreconstruct cardiac information associated with a patient's heart toindicate a source of the complex rhythm disorder by classifying beatsassociated with the cardiac information signals as high confidence beatsin response to a cycle length (CL) associated with the beats beinggreater than or equal to a minimum action potential duration (APD) andless than or equal to a maximum CL.
 38. The assembly according to claim24, wherein the computer-readable medium comprises instructions, whichwhen executed by a computing device, cause the computing device toreconstruct cardiac information associated with a patient's heart toindicate a source of the complex rhythm disorder by classifying beatsassociated with the cardiac information signals as low confidence beatsin response to a cycle length (CL) associated with the beats being lessthan a minimum action potential duration (APD) or greater than a maximumCL.
 39. The assembly according to claim 27, wherein thecomputer-readable medium comprises instructions, which when executed bya computing device, cause the computing device to reconstruct cardiacinformation associated with a patient's heart to indicate a source ofthe complex rhythm disorder by modifying the time vector using at leastone of beat shape, beat polarity, surrounding rotating emanation andradial emanation.
 40. The assembly according to claim 24, wherein thecomputer-readable medium comprises instructions, which when executed bya computing device, cause the computing device to reconstruct cardiacinformation associated with a patient's heart to indicate a source ofthe complex rhythm disorder by determining the acceptance time windowusing an expert system, the expert system using at least one of actionpotential duration (APD), conduction velocity (CV), time vectorconnecting at least two discernible activation onsets of the highconfidence signals associated with spatially adjacent sensors, frequencyand fiber angle.
 41. The assembly according to claim 24, wherein thecomputer-readable medium comprises instructions, which when executed bya computing device, cause the computing device to reconstruct cardiacinformation associated with a patient's heart to indicate a source ofthe complex rhythm disorder by determining activation onsets using anexpert system, the expert system comprising wave shapes.
 42. Theassembly according to claim 24, wherein the computer-readable mediumcomprises instructions, which when executed by a computing device, causethe computing device to reconstruct cardiac information representing acomplex rhythm disorder associated with a patient's heart to indicate asource of the complex rhythm disorder by determining activation onsetsassociated with the low confidence signals using at least one of rollingaverage and phase lock.
 43. The assembly according to claim 24, whereinthe computer-readable medium comprises instructions, which when executedby a computing device, cause the computing device to reconstruct cardiacinformation representing a complex rhythm disorder associated with apatient's heart to indicate a source of the complex rhythm disorder bydetermining activation onsets associated with the low confidence signalsusing at least two of the vector, acceptance window, rolling average,and phase lock.
 44. The assembly according to claim 24, wherein thetreatment means is selected from the group consisting of ablationenergy, stimulation energy, drug therapy, cellular therapy and genetherapy.
 45. A method of reconstructing cardiac information associatedwith a patient's heart to indicate a source of a complex rhythmdisorder, the method comprising a computing device: receiving cardiacinformation signals associated with a plurality of sensors spatiallyrelated with the patient's heart; classifying, by the computing device,the cardiac information signals into high and low confidence signals,wherein the high and low confidence signals are separated by aconfidence threshold, the confidence threshold associated with apredetermined percentage of beats having discernible activation onsetsout of total beats associated with each of the high and low confidencesignals; determining, by the computing device, an activation onsetassociated with at least one low confidence signal within an acceptancetime window, the acceptance time window associated with the lowconfidence signal indicating an earliest activation and a latesttermination for a beat in the low confidence signal; ordering, by thecomputing device, the activation onsets associated with the high and lowconfidence signals to indicate the source of the complex rhythmdisorder; and applying a treatment means to modify the indicated source.46. The method according to claim 45, wherein determining furthercomprises the computing device determining activation onsets associatedwith the low confidence signals within acceptance time windows, each ofthe acceptance time windows associated with a low confidence signalindicating an earliest activation and a latest termination for a beat inthe low confidence signal.
 47. The method according to claim 45, whereinordering further comprises the computing device ordering the activationonsets based on at least one of temporal, spatial and phase information.48. The method according to claim 45, wherein determining furthercomprises the computing device determining activation onsets associatedwith the low confidence signals using a time vector connecting at leasttwo discernible activation onsets of the high confidence signalsassociated with spatially adjacent sensors.
 49. The method according toclaim 45, wherein the complex rhythm disorder comprises no discernibleperiod during which the cardiac information signals are quiescent. 50.The method according to claim 45, wherein the complex rhythm disordercomprises no discernible earliest activation onset associated with thecardiac information signals.
 51. The method according to claim 45,wherein classifying further comprises the computing device using atleast one of activation onset, cycle length (CL), action potentialduration (APD), and amplitude to classify the cardiac informationsignals into the high and low confidence signals, wherein the activationonsets are determined by using at least one of maximum dV/dt, templatematching, frequency and amplitude.
 52. The method according to claim 45,wherein the acceptance time window is determined using at least one ofaction potential duration (APD), conduction velocity (CV), fiber angle,time vector connecting at least two discernible activation onsets of thehigh confidence signals associated with spatially adjacent sensors, andanatomic factors.
 53. The method according to claim 45, furthercomprising the computing device: removing baseline wander and noise fromthe cardiac information signals; and filtering the cardiac informationsignals.
 54. The method according to claim 45, further comprising thecomputing device disregarding at least one of the cardiac informationsignals using at least one of signal-to-noise ratio (SNR), templatematching, frequency and amplitude.
 55. The method according to claim 51,wherein template matching further comprises the computing deviceidentifying high-confidence beats associated with the cardiacinformation signals as templates.
 56. The method according to claim 51,wherein template matching is performed using an expert system, theexpert system using beat types to perform template matching.
 57. Themethod according to claim 45, further comprising the computing deviceclassifying beats associated with the cardiac information signals basedon a shape associated with the beats.
 58. The method according to claim45, wherein classifying the cardiac information signals furthercomprises the computing device classifying beats associated with thecardiac information signals as high confidence beats in response to acycle length (CL) associated with the beats being greater than or equalto a minimum action potential duration (APD) and less than or equal to amaximum CL.
 59. The method according to claim 48, further comprising thecomputing device modifying the time vector using at least one of beatshape, beat polarity, surrounding rotating emanation and radialemanation.
 60. The method according to claim 45, wherein classifying thecardiac information signals further comprises the computing deviceclassifying beats associated with the cardiac information signals as lowconfidence beats in response to a cycle length (CL) associated with thebeats being less than a minimum action potential duration (APD) orgreater than a maximum CL.
 61. The method according to claim 45, whereindetermining the acceptance time window further comprises using an expertsystem, the expert system using at least one of action potentialduration (APD), conduction velocity (CV), time vector connecting atleast two discernible activation onsets of the high confidence signalsassociated with spatially adjacent sensors, frequency and fiber angle.62. The method according to claim 45, wherein determining activationonsets further comprises using an expert system, the expert systemcomprising wave shapes.
 63. The method according to claim 45, whereindetermining activation onsets associated with the low confidence signalsfurther comprises determining activation onsets using at least one ofrolling average and phase lock.
 64. The method according to claim 45,wherein determining activation onsets associated with the low confidencesignals further comprises reconciling activation onsets determined byusing at least two of the vector, acceptance window, rolling average,and phase lock.
 65. The method according to claim 45, wherein thetreatment means is selected from the group consisting of ablationenergy, stimulation energy, drug therapy, cellular therapy and genetherapy.
 66. A system to reconstruct cardiac information associated witha patient's heart to indicate a source of the complex rhythm disorder,the system comprising: at least one computing device configured to:receive cardiac information signals from a plurality of sensorsspatially related with the patient's heart during the complex rhythmdisorder, classify the cardiac information signals into high and lowconfidence signals, wherein the high and low confidence signals areseparated by a confidence threshold, the confidence threshold associatedwith a predetermined percentage of beats having discernible activationonsets out of total beats associated with each of the high and lowconfidence signals; determine an activation onset associated with atleast one low confidence signal within an acceptance time window, theacceptance time window associated with the low confidence signalindicating an earliest activation and a latest termination for a beat inthe low confidence signal; order the activation onsets associated withthe high and low confidence signals; and output the activation onsetsassociated with the high and low confidence signals as ordered toindicate the source of the complex cardiac rhythm disorder; and a deviceconfigured to administer treatment at the indicated source.
 67. Thesystem of claim 66, wherein treatment is one of ablation, pacing, drugtherapy, cellular therapy, and gene therapy.